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Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision.

Register for the Zoom

Date and Time

Dec 4, 2025 9:00 - 11:00 AM Pacific

Benchmarking Vision-Language Models for Autonomous Driving Safety

This workshop introduces a unified framework for evaluating how vision-language models handle driving safety. Using an enhanced BDDOIA dataset with scene, weather, and action labels, we benchmark models like Gemini, FastVLM, and Qwen within FiftyOne. Our results show consistent blind spots where models misjudge unsafe situations, highlighting the need for safer and more interpretable AI systems for autonomous driving.

About the Speaker

Adonai Vera - Machine Learning Engineer & DevRel at Voxel51. With over 7 years of experience building computer vision and machine learning models using TensorFlow\, Docker\, and OpenCV. I started as a software developer\, moved into AI\, led teams\, and served as CTO. Today\, I connect code and community to build open\, production-ready AI — making technology simple\, accessible\, and reliable.

TrueRice: AI-Powered Visual Quality Control for Rice Grains and Beyond at Scale

Agriculture remains one of the most under-digitized industries, yet grain quality control defines pricing, trust, and livelihoods for millions. TrueRice is an AI-powered analyzer that turns a flatbed scanner into a high-precision, 30-second QC engine, replacing the 2+ hours and subjectivity of manual quality inspection.

Built on a state-of-the-art 8K image processing pipeline with SAHI (Slicing Aided Hyper Inference), it detects fine-grained kernel defects at scale with high accuracy across grain size, shape, breakage, discoloration, and chalkiness. Now being extended to maize and coffee, TrueRice showcases how cross-crop transfer learning and frugal AI engineering can scale precision QC for farmers, millers, and exporters. This talk will cover the design principles, model architecture choices, and a live demonstration, while addressing challenges in data variability, regulatory standards, and cross-crop adaptation.

About the Speaker

Sai Jeevan Puchakayala is an Interdisciplinary AI/ML Consultant, Researcher, and Tech Lead at Sustainable Living Lab (SL2) India, where he drives development of applied AI solutions for agriculture, climate resilience, and sustainability. He led the engineering of TrueRice, an award-winning grain quality analyzer that won India’s first International Agri Hackathon 2025.

WeedNet: A Foundation Model Based Global-to-Local AI Approach for Real-Time Weed Species Identification and Classification

Early and accurate weed identification is critical for effective management, yet current AI-based approaches face challenges due to limited expert-verified datasets and the high variability in weed morphology across species and growth stages. We present WeedNet, a global-scale weed identification model designed to recognize a wide range of species, including noxious and invasive plants. WeedNet is an end-to-end real-time pipeline that integrates self-supervised pretraining, fine-tuning, and trustworthiness strategies to improve both accuracy and reliability.

Building on this foundation, we introduce a Global-to-Local strategy: while the Global WeedNet model provides broad generalization, we fine-tune local variants such as Iowa WeedNet to target region-specific weed communities in the U.S. Midwest. Our evaluation addresses both intra-species diversity (different growth stages) and inter-species similarity (look-alike species), ensuring robust performance under real-world variability. We further validate WeedNet on images captured by drones and ground rovers, demonstrating its potential for deployment in robotic platforms. Beyond field applications, we integrate a conversational AI to enable practical decision-support tools for farmers, agronomists, researchers, and land managers worldwide. These advances position WeedNet as a foundational model for intelligent, scalable, and regionally adaptable weed management and ecological conservation.

About the Speaker

Timilehin Ayanlade is a Ph.D. candidate in the Self-aware Complex Systems Laboratory at Iowa State University, where his research focuses on developing machine learning and computer vision methods for agricultural applications. His work integrates multimodal data across ground-based sensing, UAV, and satellite with advanced AI models to tackle challenges in weed identification, crop monitoring, and crop yield prediction.

Memory Matters: Early Alzheimer’s Detection with AI-Powered Mobile Tools

Advancements in artificial intelligence and mobile technology are transforming the landscape of neurodegenerative disease detection, offering new hope for early intervention in Alzheimer’s. By integrating machine learning algorithms with everyday mobile devices, we are entering a new era of accessible, scalable, and non-invasive tools for early Alzheimer’s detection In this talk, we’ll cover the potential of AI in health care systems, ethical considerations, plus an architecture, model, datasets and framework deep dive.

About the Speaker

Reetam Biswas has more than 18 years of experience in the IT industry as a software architect, currently working on AI.

All the ML/AI in 2025! We would like to invite you for the next PyData Warsaw Meetup - 2 great speakers, tons of knowledge and discussions

19:00 - Piotr Mierzejewski, Sunscrapers - "Overview of Data Test Types in dbt: From Built-In to Custom Solutions" About Topic: Ensuring data quality and the correctness of analytical logic is a crucial part of data pipelines. DBT offers a powerful framework for testing, but it also comes with certain limitations. In this session, we will explore the various types of data tests available in DBT and discuss custom approaches you can implement to address specific challenges and tailor testing to your unique needs. About Speaker: I am a Data Engineer with over 4 years of experience and a solid academic background in computer science. Throughout my career, I have worked on projects in the petrochemical, recruitment, healthcare, and financial sectors, which has given me a broad understanding of data across different contexts. This experience allows me to quickly grasp the business goals behind the data and to manage and transform it in ways that generate the greatest value for the organization. On a daily basis, I work with Python and SQL technologies, which are my primary tools. I value these languages for their versatility and capabilities, but I am equally comfortable supporting projects that use Java, JavaScript, or C#. I am not limited to a single technology stack and can quickly adapt to new tools to complete tasks effectively. I have also supported projects from the front-end side (React) and taken on roles as a tester or team leader when project needs required it. I am confident that my flexibility and readiness to take on different roles within a team contribute to achieving the best results. I approach my work with a craftsman’s mindset, focusing on building practical, reliable solutions that solve real problems. I'm committed to constantly improving my skills, adapting to new tools, and finding efficient ways to make data truly useful. For me, success means delivering solid results and adding genuine value to each project I take on.

19:45 - Mateusz Modrzejewski, Politechnika Warszawska - "MIDI, those cheesy sounds from the 90s? Wrong! Symbolic music processing with Python"

About Topic: “MIDI, those cheesy sounds from the 90s?" is an actual question some guy asked me at an AI conference one day. What an inspiring question! This talk flips the outdated view of MIDI as retro noise, showcasing it as a powerful format for representing and analyzing music. While some think it’s obsolete, MIDI remains the backbone of modern music production and a very active research topic in machine learning. This talk unpacks MIDI’s structure and demonstrates how Python libraries like mido, pretty_midi, and MidiTok turn it into a tool for research and creativity. From visualization to music generation, practical examples reveal the modern applications of symbolic music. The takeaway? Python makes it easier than ever to explore MIDI’s potential and apply it to a wide range of musical and analytical tasks. About Speaker: Mateusz Modrzejewski, PhD Software engineer, researcher, conference speaker, author and co-author of papers on music information retrieval and audio AI. Assistant Professor at the Institute of Computer Science of Warsaw University of Technology, where he leads an Audio Intelligence Lab. Previously at Apple (Music Machine Learning team, Apple Music). Has also worked with research and engineering teams of other Fortune 500 companies, providing AI solutions and analytics. Apart from his scientific and engineering work, he is also an experienced touring musician, having performed for audiences of up to 150,000 people and having toured in Poland, China, Vietnam, the UK, Germany, Ukraine, Lithuania and Estonia, among others. Some of the artists he has played with include The Dumplings, Grubson, Marek Dyjak, Chłopcy Kontra Basia, Maria Sadowska, Pablopavo i Ludziki, Majka Jeżowska, Michał Milczarek Trio.

Venue: Centrum Innowacji Politechniki Warszawskiej, ul. Rektorska 4 Room 3.12 (3rd Floor)

DevFest Berlin is back! This year back to Humboldt University of Berlin, with more than 25 talks & workshops, you can expect a whole day of learning, socialising, and engaging with a vibrant Berlin Tech community!

🎫 Get you ticket here: pretix.eu/devfestberlin/2024/ 🖍 Call for Papers still open: pretalx.com/devfest-berlin-2024/cfp

Agenda

Day 1

9:00 AM: Registration & Coffee 🥐 ☕️

9:45 AM: 🎤 Welcoming

10:00 AM: 🎤 Katya Vinnichenko - Introduction to Google Principles of Responsible AI

This year's DevFest explores how AI can improve lives globally, from business to healthcare to education. At Google we acknowledge AI's potential, while also recognising the challenges it presents. Thus, we are committed to helping you build and use AI responsibly, ensuring fairness and ethical practices.

In my talk you will learn: the main principles of responsible AI at Google; the ethical implications of AI; best practices for developing AI systems and integrating AI into Google products and services; last but not least – how AI will change the role of the developer as we know it.

10:50 AM: 🎤 Oleksii Antypov - DMARC Demystified

Discover the essential framework behind DMARC and how it secures email communication across the internet. This session covers the historical evolution of email security, dives into the common challenges of implementing DMARC, and provides actionable best practices for protecting your domain. Ideal for developers, security professionals, and anyone interested in safe email practices.

In a world where phishing and email spoofing are constant threats, DMARC stands as a vital defense mechanism. “DMARC Demystified” takes you through a journey from the origins of email security to the modern challenges and solutions that DMARC offers. We'll explore how DMARC works with SPF and DKIM, why it’s essential for organizations of all sizes, and the practical steps to ensure smooth implementation.

Expect an interactive timeline tracing the milestones of email security, detailed breakdowns of real-world cases, and insights into optimizing DMARC. Walk away with a deeper understanding of email protection, armed with knowledge to strengthen your email systems and protect against threats.

11:40 AM: 🎤 Marcin Chudy - Demystifying App Architecture: The LeanCode Guide

At LeanCode we developed over 40 Flutter apps, spanning from huge enterprise apps to nimble startup ventures. Some were developed by a single Flutter dev, some came into light through collaborative efforts across multiple teams. Each of them was different. Each of them presented unique challenges and taught us invaluable lessons.

In this talk, we invite you to explore different approaches to architecting Flutter apps. Central to our narrative will be the concept of architectural drivers - key factors or priorities that steer our decisions about how the app is structured and designed. We'll show how we leverage our experience when approaching new projects. Drawing from our successes and failures, we'll present our current Flutter stack which enables us to craft robust, scalable, and maintainable applications. While there is no silver bullet for Flutter architecture, we can still have some sensible defaults.

Why do we use BLoC for state management? Why not Riverpod? Why do we love hook

12:30 PM: 🎤 Danny Preussler - Ten things you heard about testing that might be wrong

Testing became an essential part of Android development. Many conference talks have been given and even more best practices have been written.

But what if, as time evolved, some of the things we thought were true, changed?

Let’s start questioning some of these in this talk: Are flaky tests fixable? Are mocks even harmful? Is DI about testing? Did we understand testing in isolation properly? Is the test pyramid still valid? And in times of AI, should we generate tests?

Come and join my session to learn more!

1:10 PM: Lunch 🍔🥤

2:40 PM: 🎤 Andrey Sitnik - Privacy-first architecture: alternatives to GDPR popup and local-first

Why and how modern developers could increase the privacy of modern Web.

The popularity of clouds, the rise of huge monopolies across the internet, and the growth of shady data brokers recently have made the world a much more dangerous place for ordinary people—here is how we fix it.

In this talk, Andrey Sitnik, the creator of PostCSS and the privacy-first open-source RSS reader, will explain how we can stop this dangerous trend and make the web a private place again. — Beginners will find simple steps, which can be applied to any website — Advanced developers will get practical insights into new local-first architecture — Privacy experts could find useful unique privacy tricks from a global world perspective and beyond just U.S. privacy risks

3:30 PM: 🎤 Raphaël VO - Largest Contentful Paint - The unheard story

Largest Contentful Paint (LCP) is more than a speed metric — it's the unseen factor shaping user experiences and impacting SEO. While often overlooked, LCP reveals when a page’s core content is truly ready, affecting how users perceive load time and usability. This talk uncovers LCP’s role, why it matters more than we think, and simple strategies to boost LCP for better engagement and rankings. Discover the hidden story behind one of web performance’s most crucial, yet understated metrics.

Did you know the speed of a single webpage element could decide if users stay or leave? Largest Contentful Paint (LCP) is that hidden hero, quietly working to load the most important content quickly. This talk unveils LCP’s role in creating faster, more engaging web experiences and why it’s key to winning user loyalty. Dive into the “unheard story” of LCP and discover practical tips to make your site not only faster but unforgettable.

4:20 PM: 🎤 Ash Davies - Navigation in a Multiplatform World: Choosing the Right Framework for your App

Navigation in mobile, desktop, and web applications is such a fundamental part of how we structure our architecture. In order to both obtain functional clarity, and abstraction from platform level implementation.

For a long time, there have been options available specific to each platform, and even options part of the platform framework itself. Though it can be difficult to find the right option for platform-agnostic code, ensuring consistency. Some go one step further, providing an opinionated guide on how to architecture your application.

In this talk, I'll evaluate the options available, how they differ, and to what type of applications they are best suited. Including how to get started with them, and the best practice guidelines on how to get the most out of them, for your application.

5:10 PM: 🎤 Vadim Makeev - You don’t know MathML. Almost nobody does

Do you speak math? Me neither. Still, math formulas have always been around: from Wikipedia articles to JavaScript APIs and even CSS docs. It looks so alien that I never had a clue how to express it on the web. Apparently, there’s a markup language for that. HTML for content, SVG for vector graphics, and MathML for math! And it’s pretty cross-browser, too. Let’s dive into the basics and quirks of the language of the universe. Even if math is not your love language, you might learn something interesting about the web platform.

Day 2

9:00 AM: Registration & Coffee 🥐 ☕️

10:00 AM: 🎤 Alex Mir – Accessibility matters

The regulators are here and now businesses will care about the a11y. Let's make the a11y compliance not just a formal check. I believe that it is our job as industry experts to understand why it is important and get our products ready for all groups of people.

10:50 AM: 🎤 Marco Gomiero - From Android to Multiplatform and beyond

With Kotlin Multiplatform getting increasingly established, many Android libraries became multiplatform.

But how to make an existing Android library multiplatform?

In this talk, we will cover the common challenges faced while migrating Android libraries to Kotlin Multiplatform, like handling platform-specific dependencies, re-organizing the project structure without losing the contributor's history, testing on multiple platforms, and publishing the library.

11:20 AM: 🎤 Muhammad Salman Bediya - Crucial Performance Issue in Flutter Apps: Memory Leaks

Memory leaks can be hard to spot but have a big impact on the performance of Flutter apps, especially those running for long periods. In this talk, we’ll explore the most common reasons memory leaks happen in Flutter and Dart, focusing on how asynchronous programming and Streams can make them more challenging. You’ll learn practical tips to identify and fix these issues, helping your apps run smoother and more efficiently.

11:40 AM: 🎤 Andrii Raikov - Maximizing Scalability with Go and Redis: A Telemetry Processing Journey

At Delivery Hero, we process 10,000 requests per second using Go and Redis. Join us to learn how this powerful duo handles high-load telemetry data efficiently and cost-effectively, with scalability, resource optimization, and continuous innovation through customized data flows.

12:30 PM: 🎤 Tomek Porozynski - Can You Outsmart an AI? Adventures in Prompt Hacking

In this talk combined with hands-on elements, participants will engage in a series of live prompt hacking challenges, accessible directly through their mobile devices. The workshop begins with simple prompt injection techniques and progressively moves to more sophisticated manipulation strategies. After each successful hack, I'll analyze what made it work and transform these insights into practical defense mechanisms.

Attendees will learn: Common vulnerabilities in AI prompt design, Practical techniques for prompt injection attacks, Essential strategies for securing chatbot applications, Best practices for implementing defensive layers, Real-world examples of prompt security failures and successes

Perfect for developers working with AI models, security enthusiasts, or anyone interested in building safer AI applications. No specialized tools needed - just bring your phone and creativity! You'll leave with concrete techniques for both testing and securing your AI systems against prompt manipulation attacks.

1:10 PM: Lunch 🍔🥤

2:40 PM: 🎤 Cesar Martinez - Domain Driven Design Fundamentals for Frontend Developers

What can we learn from Domain Driven Design and how to start applying its teachings in your frontend codebase.

3:30 PM: 🎤 Vadym Pinchuk - Effortless optimization of Flutter apps: performance tips for developers

In this session, we’ll dive into effortless yet impactful ways to optimize your Flutter applications. Performance improvements don’t always require a full rewrite—sometimes, small adjustments can lead to big gains. We'll explore practical tips and tricks for enhancing app speed, responsiveness, and efficiency with minimal effort. From reducing widget rebuilds to handling large data efficiently and managing state effectively, this talk will provide developers with actionable insights to deliver a smoother user experience. Whether you’re a beginner or an experienced Flutter dev, you’ll walk away with easy-to-apply techniques to optimize your apps without breaking a sweat.

4:20 PM: 🎤 Ian Ballantyne - Generative AI on Mobile and Web with Google AI Edge

Generative AI is no longer limited to execution in the cloud. Small language models, such as Gemma 2B, are quickly becoming small and powerful enough for on-device AI, offering benefits like low latency, offline functionality, privacy, and cost-effectiveness. Google AI Edge, with MediaPipe and LiteRT (formerly Tensorflow Lite), enables the development and deployment of efficient on-device AI models. These frameworks handle the complexities of model execution and hardware acceleration, allowing developers to focus on creating innovative AI experiences.

Think generative AI is just about chatbots? Think again. This talk will go beyond basic conversations with language models and explore how on-device generative AI can be integrated into everyday apps ready to help with tasks, answer questions, and provide creative inspiration, all powered by the information located on-device. Imagine truly useful apps that are quick to respond and still work without an internet connection.

5:10 PM: 🎤 Bogdan Plieshka - Automated Testing Layers in a multidimensional Monorepo: Fast-tracking Quality for hundreds apps

In this talk, I’ll dive into the testing layers that make up our quality pipeline at Zattoo, including static analysis, unit, system, and end-to-end testing.

We’ll discuss the concept of quality gates, shift-left approach, and affected domain recognition, which helps us maintain reliability across a large, dynamic codebase, bringing total quality feedback for contributors to 3 minutes.

I’ll share practices for achieving scalable, fast testing in a high-complexity environment, offering insights for anyone working with large-scale applications or monorepos and looking to streamline QA processes.

Day 3

9:00 AM: Registration & Coffee 🥐 ☕️

10:00 AM: 🎤 Inès Mir & Doruk Deniz Kutukculer - Fellowship of Product. How your team setup affects your experience

Did you know there are 2 types of team formation in tech? These formations can change your experience in the team drastically and you better recognise them early to adjust your expectations from the job. And even more importantly, you need to show different qualities on job interviews to get this job in a particular team formation!

Deniz Doruk Kuetuekcueler, a head of engineering, and Inès Mir, a principal product designer, are trying to figure out how design and engineering can effectively work together in these setups.

10:50 AM: 🎤 Alireza Rahmaty - How we automate the App Release Monitoring at GetYourGuide

App release monitoring (ARM) represents a suite of innovative tools designed to monitor the health and stability of iOS and Android app releases. These tools provide real-time updates by sending notifications to Slack channels and logging the app's status throughout the release process. At GetYourGuide, we have developed an ARM to monitor the rollout of our Android and iOS apps from the moment they are submitted to the App Store & Google Play until they are fully released. We ship releases faster and with more confidence using ARM!

11:40 AM: 🎤 Aleksandr Gorbunov - Flutter for frontenders or There and Back Again

Every developer, regardless of specialization, may encounter the need to create a UI for a client application. The choice of technology may depend on the developer, or it may be pre-determined by the client, as happened in my case.

The peculiarity is that, coming from frontend development in JavaScript, I started building user interfaces in Flutter.

Today, there is a vast number of technologies that enable the development of cross-platform applications. These technologies are evolving rapidly, attracting large communities, and more frequently, companies are adopting them. For example, Flutter is a powerful framework that allows developers to create cross-platform applications.

With a high probability, every developer may encounter the need to use such development tools, and it’s great that frameworks like Flutter come with detailed documentation and extensive community support, making it relatively easy to start developing with them. Although, at first glance, everything might not seem smooth, and the desire to revert to familiar methods may arise.

12:05 PM: 🎤 Muhammad Salman Bediya - Crucial Performance Issue in Flutter Apps: Memory Leaks

Memory leaks can be hard to spot but have a big impact on the performance of Flutter apps, especially those running for long periods. In this talk, we’ll explore the most common reasons memory leaks happen in Flutter and Dart, focusing on how asynchronous programming and Streams can make them more challenging. You’ll learn practical tips to identify and fix these issues, helping your apps run smoother and more efficiently.

12:30 PM: 🎤 Ole Bulbuk - Native GUIs For All

Traditionally native GUIs are highly platform dependent and often specific for one programming language. In this talk we will explore a way to create GUI applications that supports virtually all platforms and any programming language. It is very effective and easy to use, too.

1:10 PM: Lunch 🍔🥤

2:40 PM: 🎤 Nicole Terc - Tap it! Shake it! Fling it! Sheep it! - The Gesture Animations Dance!

Let's have fun with animations, gestures and sensors!

Using Compose Multiplatform, we'll go over how to create animations using gestures and sensor events for Android & iOS. We'll cover some basics like how to get the device motion and position information, how to track gestures in the screen, and how you can combine them with animations to have fun!

After this talk, you'll have a better understanding on how to use the sensor frameworks, how to make your own gesture effects, and how to create interesting animations in an easy way.

Keep it fun, keep it animated!

3:30 PM: 🎤 Andrii Khrystian - From waves to widgets: Sound processing in Flutter

In this talk, we'll explore how to work with sound in Flutter apps. We'll go over the basics of adding sound effects and processing audio to make your apps more interesting. You'll learn how to handle audio files and integrate them smoothly with your Flutter projects. This session is great for anyone looking to add audio features to their apps simply and effectively.

4:20 PM: 🎤 Randy Nel Gupta - From Practice: Migration of an Order Processing System to the Cloud

A case study on how an order processing system, processing 50,000 orders daily for an international retailer spread across multiple continents and jurisdictions, is migrated to the cloud. The legacy system is implemented in PL/SQL and must be migrated during ongoing operations.

The presentation will cover all aspects from testing, monitoring, to development and the application of Site Reliability Engineering.

Furthermore, less technical topics will be introduced, such as the systematic composition of teams to ensure the necessary technical as well as domain-specific expertise.

4:50 PM: 🎤 Wietse Venema - Running open large language models in production with serverless GPUs

Many developers are interested in running open large language models, such as Google's Gemma and Llama. Open models give you full control over the deployment options, the timing of model upgrades, the private data that goes into the model, and the ability to fine-tune on specific tasks such as data extraction. Hugging Face TGI is a popular open-source LLM inference server, and Hugging Face TRL is excellent for fine-tuning. You’ll learn how to build and deploy an application that uses an open model on Google Cloud Run with cost-effective GPUs that scale down to zero instances.

Day 4

9:00 AM: Registration & Coffee 🥐 ☕️

10:00 AM: 🎤 Daniel Stamer & Diana Nanova - Workshop: From Prototype to Production

In this hands-on technical workshop participants will work on a hilarious web service prototype and deploy it to the cloud, set up build and deployment pipelines, extend the code base to leverage GenAI functionality, use SRE practices to effectively operate the application and finally strengthen the security posture of the overall software delivery process to guard against supply chain attacks.

1:10 PM: Lunch 🍔🥤

2:40 PM: 🎤 John Nguyen - Building a Chrome Extension using Gemini and Langchain

In this workshop, you will learn the basics of creating a Google Chrome Extension (which will also work on any Chromium-based Browser). We will build a simple Page summarizer using Bun, Typescript, Gemini, and LangChain. We will learn the anatomy of the manifest.json for building a Chrome Extension, Bun's bundler, how to interact with Gemini, and why LangChain is a good idea here.

3:45 PM: 🎤 Guillaume Vernade - How to make the most of Gemini multimodal capabilities?

We all know that in Tech there are always dozens of way of doing anything. But what if we could only use LLM for a first investigation? Let me show you how I'm trying to solve the mystery of who killed my pond's fishes using the power of Gemini.

Day 5

9:00 AM: Registration & Coffee 🥐 ☕️

10:00 AM: 🎤 Mario Bodemann & Joost van Dijk - Workshop: Passkeys on Android: How to get rid of passwords

Passwords. Or two factors? What about multiple factors? Which email did you register with? Why is 'password123' not working on this side, that is password is shared everywhere else?

If you recognize some of those questions, I am happy to add another couple: What are passkeys? Or how about: How to use passkeys to replace passwords in an Android app?

In this workshop I will walk through the later two questions: How to build an Android App that registers and signs users in, using passkeys. Expect a quick explanation of this fancy new technology, why it will replace passwords and how you can store them either on your mobile devices or on dedicated hardware. Following that, a fictive application and service will be built to show you how to use those passkeys and which moving pieces you will need.

Expect to use you Android Studio with Kotlin and common best practices to build an Android app, talking to the public available backend.

11:05 AM: 🎤 Anton Borries - Workshop: Adding Homescreen Widgets to Flutter Apps

HomeScreen Widgets are a great way to provide more Information to your Users right on their HomeScreens providing more ways for your App to appear in User's lives and help them achieve their goals.

In this Workshop we'll look at the necessary steps needed in order to add HomeScreen Widgets to Flutter Apps using the home_widget package

12:10 PM: 🎤 Elena Grahovac - Workshop: Mastering Multiple Engineering Leadership Roles for Maximum Impact

As an engineering manager or technical leader, navigating multiple roles that demand a diverse set of skills is a common yet challenging part of the job.

In this workshop, we will explore how to effectively balance these multiple roles and responsibilities in a complex engineering environment. Participants will be guided through the creation of their own leadership framework, tailored to adapt to the unique situations and styles of each individual. Beginning with identifying core values and responsibilities, the framework is elaborated into an actionable plan to succeed.

This workshop not only offers an opportunity for reflection on personal and professional development but also provides tools and insights to enhance management capabilities and team dynamics. Join us to cultivate a comprehensive approach to leadership that aligns with your unique role, responsibilities, and personal style.

1:10 PM: Lunch 🍔🥤

2:40 PM: 🎤 Gus Martins - Workshop: Gemma for Everyone: Your First Steps with Open Models and AI

Dive into the world of open models and AI with Gemma! This workshop will guide you through the basics of using Gemma, Google's powerful family of language models. Learn how to harness Gemma's capabilities for tasks like text generation, question answering, and more. We'll also explore how to fine-tune Gemma on your own data, allowing you to create custom AI solutions tailored to your needs. No prior experience with large language models is required!

3:45 PM: 🎤 Shahriyar Rzayev - Learn Flask the hard way: Introduce Architecture Patterns

Flask is a popular and flexible web framework for Python, but building scalable and maintainable Flask applications can be challenging without a solid understanding of architecture patterns. This workshop aims to provide participants with a detailed explanation of applying architecture patterns to Flask projects. By exploring various design principles and best practices, attendees will learn how to structure their Flask applications for improved scalability, modularity, and maintainability.

Focusing on the Repository, Unit of Work, and Use Cases patterns, attendees will gain experience in applying these patterns to enhance code organization, maintainability, and testability. All these layers are wired together using Dependency Injection, which is yet another powerful tool to use in your applications.

The application we are going to build is stored in: https://github.com/ShahriyarR/hexagonal-flask-blog-tutorial

We are going to completely rewrite the official Blog application described in Flask documentation by applying architecture patterns.

All abstraction layers are covered by unit and integration tests, which will give the attendees a detailed view of why it is important to structure the application using architecture patterns.

Speakers

Aleksandr Gorbunov - Smart Steel Technologies (Full Stack Developer)

A skilled developer specializing in JavaScript (JS) and TypeScript (TS), with strong expertise in frontend development. Proficient in the Vue ecosystem (Vue2, Vue3, Composition API, Nuxt 3), using Webpack and Vite for project bundling. Experienced in testing with Vitest, Cypress, and Jest. Adept in CSS preprocessors like SASS and Stylus. Additionally, has solid knowledge of Flutter and experie…

Andrey Sitnik - Evil Martians (Lead Engineer)

With more than 20 years in open source, Andrey Sitnik created a few popular CSS tools (PostCSS, Autoprefixer), local-first framework (Logux), and many small libraries with millions of downloads (like Nano ID).

Andrii Khrystian - Dynatrace (Senior Flutter Developer)

GDG Linz organiser. Senior Flutter Developer at Dynatrace. Public speaker and tech writer

Andrii Raikov - Delivery Hero SE (Principal Software Engineer)

Andrii is a Principal Software Engineer at Delivery Hero. He has a total of 15 years of experience with Ruby and has been very passionate about Go for the last 5 years.

Anton Borries - 1KOMMA5° (Software Engineer)

Anton is a Software Engineer working at 1KOMMA5° He loves building great UI and UX using Flutter. Coming from an Android Background the gap between Flutter and native Features has always tickled his interest. This has lead him into improving the experience of developing HomeScreen Widgets for Flutter Apps

Ash Davies

Google Developer Expert for Android, enthusiastic speaker, lead engineer at ImmobilieenScout24, Kotlin aficionado, spends more time travelling than working.

Daniel Stamer - Google (Cloud Customer Engineer)

Daniel is passionate about building modern cloud-native applications on Google's serverless technologies. He works with digital natives out of Germany’s startup capital Berlin and helps to modernize applications or build brand new ones in the cloud.

Danny Preussler - SoundCloud (Android Platform Lead)

Danny is a developer by heart, living in Berlin and leading the Android team at SoundCloud. He worked for companies like Groupon, Viacom, eBay and Alcatel and started his mobile career long before any Android with Java ME and Blackberry applications. Danny writes and talks about mobile development and testing regularly and is a Google Developer Expert for Android and Kotlin.

Elena Grahovac - FerretDB (Director of Engineering)

Elena has been in software engineering since 2007, focusing on backend systems and infrastructure. Having played the roles of both individual contributor and engineering manager, Elena is passionate about combining technical expertise with strong team collaboration. A dedicated advocate of DevOps practices, she aims to enhance workflows and bring teams together. Elena believes in helping peopl…

Gus Martins - Google (Developer Advocate)

Katya Vinnichenko - Google (Program Manager)

Katya is a Program Manager at Google Developer Relations team. Currently she is leading the Google Developer Groups across Europe, the Middle East and Africa.

Marcin Chudy - LeanCode (Senior Flutter Developer)

Marcin is a Senior Flutter Developer at LeanCode, currently playing tech lead role in a big project for the banking sector. Previously worked with backend, web frontend with React, finally settling on mobile and falling in love with Flutter at first sight. After work, he enjoys dancing salsa and bachata and attends metal concerts. Marcin is a Senior Flutter Developer at LeanCode and has …

Marco Gomiero - Airalo (Senior Android Developer | Kotlin GDE)

Marco is an Android engineer, currently working at Airalo. He is a Google Developer Expert for Kotlin, he loves Kotlin and he has experience with native Android and native iOS development, as well as cross-platform development with Flutter and Kotlin Multiplatform. In his spare time, he writes and maintains open-source code, he shares his dev experience by writing on his blog, speaking a…

Mario Bodeman - Yubico (Android Developer Advocate)

Speaker of talks, coder of code, doer of dones.

Muhammad Bediya

Muhammad Salman is a Senior Software Engineer specializing in mobile app development with a focus on building scalable, high-quality applications using Flutter, React Native, Xamarin, and Swift. With experience leading frontend teams on enterprise-level projects that have reached over 1.5 million users, he brings a strong commitment to creating impactful, user-centered solutions. A dedic…

Nicole Terc

Android GDE, Boardgame lover, videogame addict and origami enthusiast, Nicole self taught herself to code and has been fooling around with the Android ecosystem for more than 10 years. She has participated in a diverse variety of projects for several clients around the world, including video streaming, news, social media and public transport applications. Regardless of what the current adventu…

Ole Bulbuk - Ardan Labs

Ole is a backend engineer since the nineties. He has been working for many companies big and small and seen many projects fail or succeed. He loves to be part of the global Go community and working on projects that make the world a better place. In his spare time he is co-organising the Berlin chapter of GDG Golang, develops open source software and enjoys time with his family.

Oleksii Antypov - DmarcDkim.com (Founder & CEO)

Experienced CTO specializing in early-stage startups. Formerly with Rocket Internet and PocketBook, now focused on accelerating global DMARC adoption. Originally from Ukraine, I relocated to Berlin in 2015 to deepen my expertise in building successful startups from the ground up.

Raphaël VO - Ekino (Senior Software Engineer)

I’m Raphael Vo, a passionate Senior Software Engineer with over 10 years of experience, specializing in Angular and frontend development. I love turning complex ideas into delightful user experiences and tackling challenges creatively and enthusiastically. When I'm not coding, you’ll find me diving into the latest tech trends or enjoying epic board game nights with friends. As an aspiring spea…

Vadim Makeev

Frontend developer in love with the Web, browsers, bicycles, and podcasting. He/him, MDN technical writer, Google Developer Expert.

Alex Mir - mobile.de (Frontend Engineer)

Frontend Engineer at car retail platform mobile.de (part of Adevinta / ex-Ebay)

Alireza Rahmaty - GetYourGuide (Android Developer)

I am Alireza, an Android developer with 6+ years of experience building apps. I have experience building server-driven UI apps, complex UI, localisation and testing, and CI/CDI. I sometimes go hiking and play video games.

Cesar Martinez - Meyer Sound (Web Developer)

Web developer with around 10 years of experience and a passion for software architecture. Currently working at Meyer Sound.

Bogdan Plieshka - Zattoo (Principal Engineer)

Engineer with over a decade of Frontend development experience, passionate about automation, accessibility, and scaling complex systems. Working at Zattoo as a Principal Engineer, focusing on delivering frontend solutions across Web, React, and React Native for streaming media content.Organizer of the React Berlin Meetup, actively contributing to the development community.

Diana Nanova - Google (Customer Engineering Manager)

Diana is a Customer Engineering Manager at Google Cloud. Based in the German tech startup capital Berlin, Diana helps digital native customers and startups across various industries to leverage the capabilities of Google Cloud and loves championing for Google culture.

Doruk Deniz Kutukculer - Zalando (Head of Engineering)

IT professional and a leader with over 15 years of experience in the industry. Currently a Head of Engineering at Zalando.

Guillaume Vernade - Google (AI Dev Rel)

I've been a jack-of-all-trades in the Tech industry, starting as a prototyper building apps on Google Glasses and the first Android watches, then became a Product Owner and an Agile coach. I realized my childhood dream of becoming a video game producer then came back to my other passion: AI.

Ian Ballantyne - Google (AI DevRel)

Ian is a Developer Relations Engineer for AI at Google. Currently he works on generative AI, such as Gemini and Gemma. He is passionate about on-device AI, using technologies such as Google AI Edge to deploy artificial intelligence to web and mobile devices. He has been in Developer Relations at Google for 9 years specializing in helping partners and developers unlock the capability of Google …

Inès Mir - Zalando (Principal Product Designer)

A principal product designer at Zalando and a content creator.

John Nguyen - Eon (Backend Developer)

Fullstack developer with a knack for whipping up code recipes using my secret ingredients: a dash of JavaScript, a pinch of Python, and a whole lot of serverless magic John's journey in software development began as a PHP developer, but he later transitioned to front-end development and became passionate about all things related to Javascript. While working as a data DevOps engineer in a…

Joost van Dijk - Yubico (Developer Advocate)

Joost van Dijk is a developer advocate at Yubico. As the inventor of the YubiKey, Yubico makes secure login easy and available for everyone. Joost focuses on securing digital identities and accelerating the adoption of open authentication standards as part of Yubico’s developer program.

Randy Gupta

Randy is a Google Developer Expert for Cloud and also Organizer of the GDG Düsseldorf. With a professional experience of more 25 years in software development he is focused today on building microservices applications on top of Kubernetes.

Shahriyar Rzayev - Nord Security (Senior Software Engineer)

Senior Software Engineer @ Nord Security. Moving forward on Clean Code and Clean Architecture. Previous accomplishments include contributing to open source, providing technical direction, and sharing knowledge about Clean Code and Architectural patterns. An empathetic team player and mentor. Azerbaijan Python Group Leader. Former QA Engineer and Bug Hunter.

Tomek Porożyński - Atos

Vadym Pinchuk - Sky (Mobile Software Engineer)

Vadym, a seasoned software engineer, possesses a wealth of experience in Android application development. He has skillfully transitioned his expertise to cross-platform development, utilizing Flutter. Throughout his career, Vadym has collaborated with a diverse range of companies, from industry giants like Samsung, Volvo, Bosch, and Instagram to smaller start-ups. Leveraging his extensiv…

Wietse Venema - Google (Google Cloud Engineer)

Wietse Venema is an engineer at Google Cloud. He wrote the O’Reilly book on Cloud Run.

Hosts

Seemran Xec - Sawayo (Software Engineer)

A focused developer possessing professional experience of 6+ years in software development for product-based and service-based industries, with businesses acquiring valuable insight and implementing best practices. Collaborated with startups and other businesses as a freelancer/consultant to build, design, and manage the product. I'm passionate about what I do and a lifelong learner.

Louis Tsai - Zalando SE (GDG Organizer)

Alex Mir - mobile.de (Frontend Engineer)

Frontend Engineer at car retail platform mobile.de (part of Adevinta / ex-Ebay)

Jhoon Saravia - Greenmates (Mobile Engineer)

Software consultant and developer, experienced in Android, Flutter and Full-stack. Interested in working on DEI initiatives as a complement to my core work. Particularly interested in technology, gadgetry, the future, the combination of those three and the impact that driving Diversity, Equity and Inclusion has on all of them both in and out of the workplace.Amateur photographer a…

Matthias Geisler - Thermondo (Senior Software Engineer)

True believer in (Kotlin) Multiplatform and working with it for over 4 years now. Builds solutions for Android. Maintainer and developer of KMock. Co-Organizer of KUG Berlin, GDG Android Berlin, Rust Berlin and XTC Berlin.

Emy Jamalian - Atlas Metrics (Software QA Engineer)

Complete your event RSVP here: https://gdg.community.dev/events/details/google-gdg-berlin-presents-devfest-berlin-2024/.

Speaker: Mathias Thierbach Start Date: Tue, Oct 22nd 2024 · 7:00 PM EEST (4:00 PM UTC) Language: ENGLIGH Location: Online (link visible for attendees)

===============================================================

Description:

In this session, we’ll explore the powerful concept of Git branching and how it can elevate your development processes within Microsoft Fabric and Power BI projects.

We'll start by introducing the fundamentals of Git branching, highlighting how it enhances collaboration, improves quality control, and enables versioning in professional development environments. You’ll learn why branches are essential for any development team aiming to streamline workflows and maintain project stability.

Next, we’ll take a closer look at Fabric’s branching capabilities, specifically the "branch-out" feature. We’ll dive into its strengths, where it simplifies your workflow, and where additional steps or workarounds may be necessary to achieve optimal results. By understanding these nuances, you’ll be better equipped to make the most of Fabric’s tooling in your own projects.

Finally, we’ll explore branching in the context of various industry-standard Git workflows. Whether you’re familiar with Git Flow, GitHub Flow, or other branching strategies, you’ll see how these practices apply to Fabric and Power BI projects. We'll tie it all together with practical examples, helping you understand how different development flows function in real-world scenarios, and how to choose the right strategy for your team’s needs.

By the end of this talk, you’ll have a clear understanding of how Git branches can transform your development processes in Fabric and Power BI, along with actionable insights on how to implement them in your day-to-day workflows.

===============================================================

At the end of the Meetup we'll have a Raffle with prizes offered by Enterprise DNA: 2 (two) 1 year FREE Membership Licenses on EDNA Platform for two lucky winners from the Live attendees !

===============================================================

Speaker: Mathias Thierbach Microsoft Data Platform MVP

Mathias is a Microsoft Data Platform MVP with a deep expertise in DevOps workflows and developer tooling, across .Net software projects, Power BI, and Microsoft Fabric.

With over 20 years of diverse experience spanning technical, managerial, and commercial roles, he is passionate about advancing CI/CD and DevOps practices. Mathias is on a mission to empower data engineering teams of all sizes by providing better tools and training that promote mature, efficient collaboration and development patterns. As the creator and maintainer of pbi-tools, he has also made significant contributions to the Power BI community, including the development of the TMDL definition language for semantic models.

Connect with Mathias here:

• LinkedIn

0:00

hi everyone Welcome to our event this event is brought to you by data dos club which is a community of people who love

0:06

data and we have weekly events and today one is one of such events and I guess we

0:12

are also a community of people who like to wake up early if you're from the states right Christopher or maybe not so

0:19

much because this is the time we usually have uh uh our events uh for our guests

0:27

and presenters from the states we usually do it in the evening of Berlin time but yes unfortunately it kind of

0:34

slipped my mind but anyways we have a lot of events you can check them in the

0:41

description like there's a link um I don't think there are a lot of them right now on that link but we will be

0:48

adding more and more I think we have like five or six uh interviews scheduled so um keep an eye on that do not forget

0:56

to subscribe to our YouTube channel this way you will get notified about all our future streams that will be as awesome

1:02

as the one today and of course very important do not forget to join our community where you can hang out with

1:09

other data enthusiasts during today's interview you can ask any question there's a pin Link in live chat so click

1:18

on that link ask your question and we will be covering these questions during the interview now I will stop sharing my

1:27

screen and uh there is there's a a message in uh and Christopher is from

1:34

you so we actually have this on YouTube but so they have not seen what you wrote

1:39

but there is a message from to anyone who's watching this right now from Christopher saying hello everyone can I

1:46

call you Chris or you okay I should go I should uh I should look on YouTube then okay yeah but anyways I'll you don't

1:53

need like you we'll need to focus on answering questions and I'll keep an eye

1:58

I'll be keeping an eye on all the question questions so um

2:04

yeah if you're ready we can start I'm ready yeah and you prefer Christopher

2:10

not Chris right Chris is fine Chris is fine it's a bit shorter um

2:18

okay so this week we'll talk about data Ops again maybe it's a tradition that we talk about data Ops every like once per

2:25

year but we actually skipped one year so because we did not have we haven't had

2:31

Chris for some time so today we have a very special guest Christopher Christopher is the co-founder CEO and

2:37

head chef or hat cook at data kitchen with 25 years of experience maybe this

2:43

is outdated uh cuz probably now you have more and maybe you stopped counting I

2:48

don't know but like with tons of years of experience in analytics and software engineering Christopher is known as the

2:55

co-author of the data Ops cookbook and data Ops Manifesto and it's not the

3:00

first time we have Christopher here on the podcast we interviewed him two years ago also about data Ops and this one

3:07

will be about data hops so we'll catch up and see what actually changed in in

3:13

these two years and yeah so welcome to the interview well thank you for having

3:19

me I'm I'm happy to be here and talking all things related to data Ops and why

3:24

why why bother with data Ops and happy to talk about the company or or what's changed

3:30

excited yeah so let's dive in so the questions for today's interview are prepared by Johanna berer as always

3:37

thanks Johanna for your help so before we start with our main topic for today

3:42

data Ops uh let's start with your ground can you tell us about your career Journey so far and also for those who

3:50

have not heard have not listened to the previous podcast maybe you can um talk

3:55

about yourself and also for those who did listen to the previous you can also maybe give a summary of what has changed

4:03

in the last two years so we'll do yeah so um my name is Chris so I guess I'm

4:09

a sort of an engineer so I spent about the first 15 years of my career in

4:15

software sort of working and building some AI systems some non- AI systems uh

4:21

at uh Us's NASA and MIT linol lab and then some startups and then um

4:30

Microsoft and then about 2005 I got I got the data bug uh I think you know my

4:35

kids were small and I thought oh this data thing was easy and I'd be able to go home uh for dinner at 5 and life

4:41

would be fine um because I was a big you started your own company right and uh it didn't work out that way

4:50

and um and what was interesting is is for me it the problem wasn't doing the

4:57

data like I we had smart people who did data science and data engineering the act of creating things it was like the

5:04

systems around the data that were hard um things it was really hard to not have

5:11

errors in production and I would sort of driving to work and I had a Blackberry at the time and I would not look at my

5:18

Blackberry all all morning I had this long drive to work and I'd sit in the parking lot and take a deep breath and

5:24

look at my Blackberry and go uh oh is there going to be any problems today and I'd be and if there wasn't I'd walk and

5:30

very happy um and if there was I'd have to like rce myself um and you know and

5:36

then the second problem is the team I worked for we just couldn't go fast enough the customers were super

5:42

demanding they didn't care they all they always thought things should be faster and we are always behind and so um how

5:50

do you you know how do you live in that world where things are breaking left and right you're terrified of making errors

5:57

um and then second you just can't go fast enough um and it's preh Hadoop era

6:02

right it's like before all this big data Tech yeah before this was we were using

6:08

uh SQL Server um and we actually you know we had smart people so we we we

6:14

built an engine in SQL Server that made SQL Server a column or

6:20

database so we built a column or database inside of SQL Server um so uh

6:26

in order to make certain things fast and and uh yeah it was it was really uh it's not

6:33

bad I mean the principles are the same right before Hadoop it's it's still a database there's still indexes there's

6:38

still queries um things like that we we uh at the time uh you would use olap

6:43

engines we didn't use those but you those reports you know are for models it's it's not that different um you know

6:50

we had a rack of servers instead of the cloud um so yeah and I think so what what I

6:57

took from that was uh it's just hard to run a team of people to do do data and analytics and it's not

7:05

really I I took it from a manager perspective I started to read Deming and

7:11

think about the work that we do as a factory you know and in a factory that produces insight and not automobiles um

7:18

and so how do you run that factory so it produces things that are good of good

7:24

quality and then second since I had come from software I've been very influenced

7:29

by by the devops movement how you automate deployment how you run in an agile way how you

7:35

produce um how you how you change things quickly and how you innovate and so

7:41

those two things of like running you know running a really good solid production line that has very low errors

7:47

um and then second changing that production line at at very very often they're kind of opposite right um and so

7:55

how do you how do you as a manager how do you technically approach that and

8:00

then um 10 years ago when we started data kitchen um we've always been a profitable company and so we started off

8:07

uh with some customers we started building some software and realized that we couldn't work any other way and that

8:13

the way we work wasn't understood by a lot of people so we had to write a book and a Manifesto to kind of share our our

8:21

methods and then so yeah we've been in so we've been in business now about a little over 10

8:28

years oh that's cool and uh like what

8:33

uh so let's talk about dat offs and you mentioned devops and how you were inspired by that and by the way like do

8:41

you remember roughly when devops as I think started to appear like when did people start calling these principles

8:49

and like tools around them as de yeah so agile Manifesto well first of all the I

8:57

mean I had a boss in 1990 at Nasa who had this idea build a

9:03

little test a little learn a lot right that was his Mantra and then which made

9:09

made a lot of sense um and so and then the sort of agile software Manifesto

9:14

came out which is very similar in 2001 and then um the sort of first real

9:22

devops was a guy at Twitter started to do automat automated deployment you know

9:27

push a button and that was like 200 Nish and so the first I think devops

9:33

Meetup was around then so it's it's it's been 15 years I guess 6 like I was

9:39

trying to so I started my career in 2010 so I my first job was a Java

9:44

developer and like I remember for some things like we would just uh SFTP to the

9:52

machine and then put the jar archive there and then like keep our fingers crossed that it doesn't break uh uh like

10:00

it was not really the I wouldn't call it this way right you were deploying you

10:06

had a Dey process I put it yeah

10:11

right was that so that was documented too it was like put the jar on production cross your

10:17

fingers I think there was uh like a page on uh some internal Viki uh yeah that

10:25

describes like with passwords and don't like what you should do yeah that was and and I think what's interesting is

10:33

why that changed right and and we laugh at it now but that was why didn't you

10:38

invest in automating deployment or a whole bunch of automated regression

10:44

tests right that would run because I think in software now that would be rare

10:49

that people wouldn't use C CD they wouldn't have some automated tests you know functional

10:56

regression tests that would be the

dbt Meetups are networking events open to all folks working with data! Talks predominantly focus on community members’ experience with dbt, however, you’ll catch presentations on broader topics such as analytics engineering, data stacks, DataOps, modeling, testing, and team structures.

📍 Venue Host: Utopicus Habana (P.º de La Habana, 9, 11, 28036 Madrid) 🍕 Catering: Drinks & Pizza at the place of the event 🤝 Organizer: Astrafy is organizing this event, enabled by the community team at dbt Labs

*To attend, please read the Health and Safety Policy and Terms of Participation: ***https://www.getdbt.com/legal/health-and-safety-policy 🗓️Agenda:

• 19h00: welcome
• 19h05: dbt semantic layer (Charles Verleyen & Alejandro de la Cruz Lopez - Astrafy)
• 19h35: break
• 19h45: Standardizing pipelines with DBT without dying trying (Ruben Ibañez Pinillo & Miguel Martin Tapia - Mercadona Tech)
• 20h15: networking with drinks & pizzas 🍺 🍕

🗣️Presentation #1: We will explore the concepts of a semantic layer and why it has been gaining traction recently. We will talk about why it is a foundation pillar for LLM and finish with a hands-on demo. Speakers bio: Charles is a data architect/engineer and has been working for the last 10 years in different industries before founding his company recently with a focus on providing modern data services. Alejandro is a Data architect passionate about data, automation, and AI. He has recently shifted left on the data engineering aspect of data and is a savvy dbt engineer. --- 🗣️Presentation #2: The talk explores the benefits and challenges of using DBT, a powerful tool for building and managing data transformations, to achieve standardization without sacrificing sanity. We will see best practices for leveraging DBT's features to streamline data pipelines, improve data quality, and achieve consistency across an organization's data infrastructure.

➡️ Join the dbt Slack community: https://www.getdbt.com/community/ dbt is a data transformation framework that lets analysts and engineers collaborate using their shared knowledge of SQL. Through the application of software engineering best practices like modularity, version control, testing, and documentation, dbt’s analytics engineering workflow helps teams work more efficiently to produce data the entire organization can trust.Learn more: https://www.getdbt.com/

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.

Zoom Link

https://voxel51.com/computer-vision-events/september-14-meetup/

ARMBench: An Object-Centric Benchmark Dataset for Robotic Manipulation

Amazon Robotic Manipulation Benchmark (ARMBench), is a large-scale, object-centric benchmark dataset for robotic manipulation in the context of a warehouse. ARMBench contains images, videos, and metadata that corresponds to 235K+ pickand-place activities on 190K+ unique objects. The data is captured at different stages of manipulation, i.e., pre-pick, during transfer, and after placement from a robotic workcell in an Amazon warehouse. Benchmark tasks are proposed by virtue of high-quality annotations and baseline performance evaluation are presented on three visual perception challenges, namely 1) object segmentation in clutter, 2) object identification, and 3) defect detection. ARMBench can be accessed at http://armbench.com.

Chaitanya Mitash is Sr. Applied Scientist at Amazon Robotics. His research focuses on computer vision and manipulation for item manipulation. He received his Ph.D. in computer science from Rutgers University.

Fan Wang is a Research Scientist at Amazon Robotics, with a focus on robotic manipulation and perception. She holds a Ph.D. in electrical and computer engineering from Duke University. Her undergraduate degree was in electrical and mechanical engineering from the University of Edinburgh, UK.

Mani Nambi is a Sr. Applied Scientist at Amazon Robotics. His research focuses on manipulation systems for item and package handling. He received his Ph.D. in mechanical engineering from the University of Utah.

From Model to the Edge, Putting Your Model into Production

This talk delves into the journey from model training to deployment at the edge – an often neglected yet vital aspect of machine learning implementation. It elucidates the essential practices and challenges associated with transitioning an AI model from a controlled environment to real-world edge devices.

Joy is a Machine learning Engineer at Secury360, a startup offers a hardware box that turns your security cameras into a perimeter security system with no false detections. He is responsible for the model training, active learning infrastructure and managing the labeling team. If you have been in the FiftyOne Slack you probably have seen him around.

Optimizing Distributed Fine-Tuning Workloads for Stable Diffusion with the Intel Extension for PyTorch on AWS

In this talk, we explore the use of the Intel Extension for PyTorch to optimize a vision generative AI workload. The vision workload focuses on the fine-tuning of a stable diffusion model, on the AWS cloud using Intel’s 4th Generation Xeon Processors. We leverage optimizations like Intel Advanced Matrix Extensions (AMX) and mixed-precision with BF16 and FP32, to speed up training. Attendees can expect (1) A technical dive into the workload and solution (2) a brief code walkthrough (3) workload setup on AWS, and (4) a short demo.

Eduardo Alvarez is a Senior AI Solutions Engineer at Intel and a specialist in applied deep learning and AI solution design. His background includes building software tools for the energy sector, and his primary interests lie in time-series analysis, computer vision, and cloud solutions architecture. Additionally, he is a community leader in data science and ML/AI for geosciences.