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| Title & Speakers | Event |
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 20:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
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Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 20:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
|
|
Dec 4 - AI, ML and Computer Vision Meetup
2025-12-04 · 17:00
Join the virtual Meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. 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. |
Dec 4 - AI, ML and Computer Vision Meetup
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Berlin Cybersecurity Social #18: AI & Cybersecurity Sessions
2025-07-31 · 15:00
Are you a cybersecurity professional looking to connect with like-minded professionals, share experiences, and make friends? Look no further! Join us for a special edition of the Berlin Cybersecurity Social hosted in collaboration with the Venture Café Berlin and the AI Ethics Action Hub for a fantastic evening of networking. Agenda:
*This session is organised by the AI Ethics Action Hub About the Speakers: Iryna Schwindt is a Cybersecurity engineer currently at Vodafone and a co-author at the OWASP AI Exchange (https://owaspai.org/) project, contributing to the EU AI Act security standard and AI Red Teaming. Jose Quesada is the founder and director of Data Science Retreat (DSR), an advanced ML bootcamp that has helped over 300 professionals land data science roles. With a PhD and 20+ years in machine learning, Jose brings a unique blend of technical depth and creative flair—he’s also a former photorealism artist. He has advised on impactful projects ranging from malaria diagnostics to sustainability-focused robotics. Diana Waithanji is a Cybersecurity Engineer at SAP SE, with experience working across Europe and Africa. She is an advocate for data privacy as a fundamental human right and serves on two technical committees at the Kenya Bureau of Standards. Diana is also a board member at Nivishe Foundation, where she supports youth mental health through safe spaces. Her work bridges global standards, social impact, and cutting-edge security practices. Ali Yazdani is a seasoned security professional with over a decade of experience spanning offensive security and secure development practices. Starting his career as a penetration tester, he now specializes in building scalable DevSecOps programs and embedding security into engineering workflows. Ali brings deep technical knowledge and a pragmatic approach to security culture. His mission is to empower teams to build safer software at scale and is currently a founder at Scandog.io Pranav Vattaparambil is Chief Security Officer at Unosecur (https://www.unosecur.com/) as well as a security and product strategist with deep expertise in fintech. Formerly VP of Cybersecurity at the EU’s largest Banking-as-a-Service company, he also advises multiple startups on navigating security, risk, and go-to-market strategy. Pranav bridges the gap between technical execution and business impact, especially in regulated industries like banking and crypto. His focus is on helping companies build secure, scalable products from day one. About Venture Café Berlin: Venture Café Berlin connects a community of innovators and entrepreneurs with free high-impact programming and events. Venture Café is a part of the CIC network, whose mission is to fix the world through innovation. About Berlin Cybersecurity Social: This meetup is open to cybersecurity professionals of all levels, from beginners to experts. Whether you're a seasoned pro or just starting your journey in the field, this event is the perfect opportunity to connect with others who share your passion for cybersecurity. About the AI Ethics Action Hub: A global, interdisciplinary collective dedicated to advancing ethical, inclusive, and accountable AI. We believe technology should be designed to respecting human dignity, planetary well-being, and intergenerational justice. |
Berlin Cybersecurity Social #18: AI & Cybersecurity Sessions
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Data Engineering Meetup - Data Across Industries
2025-07-16 · 17:00
We’ve got something new coming to the Data Engineering Meetup this summer! This time around, we’re planning our biggest event yet—and we’re adding a new feature: a panel discussion with experts from the field, talking about where data is headed and what’s next for the industry. We’ll be sharing more details soon, but spots are limited, so be sure to grab your ticket before they run out! When? 18:00 - 18:30 Networking with food and drinks from Dremio 18:30 - 20:00 Talk + Panel discussion 20:00 - 20:30 More networking Where? Dremio offices (see address) Speakers and Talks: Opening Talk: "Developing AI-Ready Data Products with Dremio and dbt" - Ashley Farrugia, Solutions Architect @ Dremio 🎤 Panel Topic: Pitfalls of Large Enterprises on Data Decisions When big budgets meet big ambitions, things can get messy. Large enterprises often struggle to balance innovation with scale, managing trade-offs around security, cost, and long-term maintainability. This panel dives deep into the common missteps organisations make in their data strategies—and where they should be focusing to build smarter, more resilient foundations. Panelists:
🎤 Panel Topic: Visions of the Future (Moderator: Zoe (Ziwen) Qin, Vice President at Dawn Capital) What does the next five years of data look like, and are we ready? This panel dives into the evolving future of data, exploring the growing role of AI, the strategic decisions that will need to be made, and the blind spots companies can’t afford to ignore. Our panelists will dive into the trends, tensions, and transformations shaping tomorrow’s data landscape.
Places are limited, make sure you register! Note: Please ensure your RSVP status is kept up to date, as this helps us offer spots to those on the waitlist. Please be aware that if you have three or more no-shows, you may be ineligible to attend future events. |
Data Engineering Meetup - Data Across Industries
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Data Engineering Meetup - Data in Fintech
2025-05-22 · 17:00
After a long break, the Data Engineering Meetup is back with the sun! ☀️ This year, we'll be diving into different industries to learn how data engineering is applied across a wide range of sectors. It's the perfect opportunity to network, learn new things, and engage in interesting discussions. When? 18:00 - 18:30 Networking with food and drinks from GoCardless 18:30 - 19:45 Talks 19:45 - 20:30 More networking Where? GoCardless offices (see address) Speakers and Talks: 1. Why Data Contracts Are the Foundations of a Data Mesh - by Andrew Jones (Principal Engineer @ GoCardless) 2. Data as Differentiator: Building Fintech Flywheels - by Marv Gillibrand (Head of Product @ Colibri) 3. Microseconds to Milliseconds: Dual-Latency Trading Stacks on AWS - by Avinash Mutham (Director @ AvYamsh Ltd) If there's a topic you're passionate about and would like to see discussed, let us know! We're always looking for more talks for our future events. Places are limited, make sure you register! |
Data Engineering Meetup - Data in Fintech
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Unlock Smarter Asset Insights with Graph-Powered Financial Data Analysis
2025-05-20 · 22:30
Join us for our next developer meetup! Unlock Smarter Asset Insights with Graph-Powered Financial Data Analysis Tired of traditional data pipelines that miss crucial connections in financial services? Join us for an insightful evening exploring how Knowledge Graphs are revolutionizing data analysis for hedge funds and financial institutions. Alessandro Pireno, Solutions Director at SurrealDB, will demonstrate how graph-based approaches deliver richer, faster, and more contextual insights into asset ownership, market movements, and SMAs. Discover how to:
Whether you're in hedge funds, fintech, or passionate about AI-driven finance, this meetup offers a fresh perspective on leveraging graphs for smarter decision-making. Agenda:
Host: Alessandro Pireno Solutions Director, SurrealDB \| LinkedIn Alessandro is a seasoned product development and solutions leader with a proven track record of building and scaling data-driven solutions across diverse industries. He has led product strategy and development at companies like HUMAN and Omnicom Media Group, optimized data collection and distribution at GroupM, and was an early leader of success at Snowflake. With a deep understanding of the challenges and opportunities facing today’s tech landscape, Alessandro is passionate about empowering organizations to unlock the full potential of their data through innovative database solutions. Guest speaker: Andrey Zelenovsky AI Enthusiast \| LinkedIn Andrey Zelenovsky is an industry practitioner in Robotic Process Automation and AI. Holding Bachelor Degrees in Information and Systems Engineering, and Analytical Finance, he has been custom-tailoring software solutions used by many of the S&P 500 companies today. After receiving his Data Science Master’s, he joined one of today’s fastest growing software start-ups, UiPath, whose Robotic Process Automation software liberates today’s workforce from the mundane and changes the paradigm of work. He is eager to pull together what he’s learned from Lean Six Sigma, Operations Research, Data Science, and his most recent experience of orchestrating work between people and machines to positively transition humanity into the future. -- 👉 New to SurrealDB? Get started here. 🗣️ Speaker opportunities - submit your talk! Working on an interesting project that you would like to share with the community? Submit your talk here. FAQs Is the venue accessible? The Yard is located on the 2nd floor. When you arrive, just let security know that you're heading up to The Yard. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket. |
Unlock Smarter Asset Insights with Graph-Powered Financial Data Analysis
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DevOps Society Birmingham Q2 2025
2025-04-03 · 17:00
Details Our first Birmingham DevOps Society meetup event of 2025 has arrived! We are delighted to be hosted by BJSS, in Birmingham city centre. In this event, we will be hearing from two exciting speakers. Jason Content, will be going through building cloud infrastructure as code, while Mateusz will be diving into what Express Route Cross Connects are! Format of the meetup event will be as follows: 6pm – 6:30pm - Networking on arrival coupled with pizza and drinks provided by ReVybe IT. 6:30pm – 7pm – First speaker will take to the stage. 7pm – 7:10pm – Break for refreshments and networking 7:10pm – 7:40pm – Second speaker will take to the stage. 8pm onwards – Networking at event space and a local pub afterwards. We are delighted to welcome 2 speakers to our second Birmingham event. #1 – Jason Content – Senior / Lead DevOps Engineer @ Cority Jason has been a DevOps engineer since the early days of AWS, when YAML was still unfamiliar and Terraform didn’t exist. Over the years, he has led DevOps teams and designed solutions across multiple cloud providers, including AWS, Azure, GCP, Hetzner Cloud, and on-premises platforms like Azure Arc. Title - Empowering Developers: Building Cloud Infrastructure with Code Jason will be sharing his experience at Cority, where he redesigned and coded their AWS infrastructure using Python. The talk will explore how we can empower developers to build infrastructure themselves, removing the DevOps or platform engineering team as a bottleneck. Current tools like Terraform, OpenTofu, AWS CloudFormation, and Azure Bicep often put platform engineering teams at the centre, creating potential delays and resource challenges. Instead, he will discuss how teams can shift this responsibility to developers by equipping them with the right tools and frameworks to create infrastructure independently using any programming language. This approach reduces bottlenecks, speeds delivery, and fosters team collaboration. Jason also runs a blog, theclouddude.co.uk, where he shares insights and lessons learned from his work in the DevOps space. Writing for the blog allows him to give back to the DevOps and developer communities by fostering discussions and sharing practical solutions. #2 – Mateusz Golebiewski - Platform Engineer @ BJSS Mateusz is a Platform Engineer at BJSS (now part of CGI), where he helps build and secure cloud platforms for clients across government, aviation, and financial industries. With a strong background in cloud technologies, he is passionate about designing scalable, resilient, and secure solutions. Title - Beyond Peering: Unlocking the Power of ExpressRoute Cross Connects As businesses move more of their mission critical applications to the cloud, performance, reliability and security become utmost priorities. In this session, we'll explore what are Express Route Cross Connects and how they go beyond traditional peering, providing direct, fast and secure connection between Azure and on-premise data centers. P.S. Please provide full name and email address for access to the BJSS venue! Address - 37 Temple Street, Birmingham, B2 5DP |
DevOps Society Birmingham Q2 2025
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Meetup #6
2025-03-26 · 18:00
Hi Everyone, Thank you once again for your amazing support throughout 2024 and for joining us at our last event in October – it was a fantastic turnout! We’re thrilled to announce that Meet-Up #6 is now confirmed for Wednesday 26th March (our first event for 2025)! 🎉 Join us at our London HQ, 100 Liverpool Street, for an evening packed full of insightful talks, great conversations and plenty of opportunities to connect with like-minded individuals. Of course, it wouldn’t be a proper meet-up without pizza and refreshments to keep the energy high all night! 🍕🥤 🕕 Doors open for networking at 18:00 🎤 The first talk kicks off at 18:30 🚪 Wrapping up at 20:30 We can’t wait to see you there! #1 Maryam Aidini \| Principal Product Manager @ Mesh-AI "Building Smarter Data & AI Products with Lean Experimentation" Maryam is a Principal Product Manager with a passion for building innovative, data-driven products. With over 12 years of experience, she has led high-performing teams and helped companies craft smart, effective product strategies that align with both business goals and customer needs. Currently working at an AI and data consultancy, Maryam focuses on using AI and advanced technologies to enhance decision-making, streamline processes, and create scalable, customer-centric solutions. She’s a strong advocate for data-driven product management, believing that the best products come from a mix of strategic thinking, deep user understanding, and the right use of technology. Beyond her work in product management, Maryam is also a course creator and presenter with O’Reilly, where she helps upskill product managers in AI, data, and product strategy. She is passionate about teaching and mentoring, sharing practical insights to help product professionals stay ahead in the rapidly evolving AI landscape. #2 Rebecca Vickery \| Senior Leader of Customer Insight & Targeting @ EDF Energy "SmartGuard: Optimising Smart Field Engineering with AI" Rebecca is the Senior Leader of EDF's Customer Insight & Targeting team, a Data Science and Research team dedicated to driving better customer engagement through data insights. With over 16 years of experience in the field of Data Science Rebecca mentors and leads her team to develop models for segmentation, targeting and personalisation. Passionate about technology and improving customer experiences she is currently leading a project to embed a new Customer Engagement Platform (CEP) that will streamline and improve customer communications across the organisation. Outside of her day job Rebecca is a strong advocate for improving gender diversity in the data and tech fields. She also regularly publishes articles designed to help people break into the field of Data Science on her blog hosted on Medium.com. #3 Zoe Higgins \| Data Analytics Manager @ Santander "Am I valued or just a metric?" In this talk, I’ll explore the intersection of data, culture, and inclusion in STEM, drawing from my experience as a data scientist and my recent work in human resources. With access to the numbers and drivers shaping workplace culture, we’ll uncover what truly influences representation and belonging. Beyond the statistics, we’ll reflect on overcoming imposter syndrome, challenging stigmas, and ensuring that women in AI and data are not just counted—but truly valued. Zoe Higgins is a Data Analytics Manager at Santander, with six years of experience leveraging data science to drive insights and innovation. Holding a Bachelor of Science in Data Science, she has built deep expertise in analytics, strategy, and problem-solving working on global projects from UK, Spain and Poland. Beyond her technical work, Zoe is a passionate advocate for equitability within STEM industries, working to create more inclusive opportunities. She brings her insights on data-driven decision-making and the importance of fostering diversity in tech and leveraging data to build the right culture. |
Meetup #6
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Unlocking Fabric Automation: Lessons, Possibilities, and Real-World Insights
2025-01-16 · 18:00
Join our Microsoft Fabric - UK User Group for our latest webinar. This session we are excited to announce our host Microsoft MVP Leon Gordon and co-host Microsoft MVP Pragati Jain will be introducing Hector Sven Manager, Data Engineering @ Avanade. Join the Microsoft Fabric UK - User Group on - Microsoft Community - https://community.powerbi.com/t5/Power-BI-UK-User-Group/gh-p/pbi_UK_usergroup LinkedIn - https://www.linkedin.com/groups/8909321/ MeetUp - https://www.meetup.com/microsoft-power-bi-uk/ Session Abstract - As Microsoft Fabric continues to grow as a unified data platform, the need for automation in managing and deploying Fabric environments has never been greater. In this session, I’ll introduce FabricCatalyst, a PowerShell-driven solution that automates and streamlines the creation and deployment of Fabric items by leveraging the Fabric REST API. I'll show you the three deployment approaches enabled by FabricCatalyst: Auto, Custom & Map; throughout the session, I’ll share my personal experience building FabricCatalyst, including Real-world use cases (When and why each deployment method is most useful) and insights into current gaps in the Fabric API and where workarounds were necessary. By the end of this session, you’ll leave with a broader understanding of the automation possibilities in Microsoft Fabric, practical insights into real-world challenges, and ideas to inspire your own automation journey. Biography ential of Microsoft technologies; with more than 20 years' worth of experience in the IT industry, I've spent the last 8 immersing myself in the world of Microsoft Azure, specializing in the field of data management, analytics and business intelligence working with technologies such as SQL Databases, Synapse, Data Factory, Analysis Services and Power BI. I hold six Azure Associate-level certifications (Developer, Data Engineer, DB Admin, Power BI Data Analyst & Fabric Analytics Engineer), complemented by a DevOps Engineer Expert credential. As a consultant for Accenture/Avanade, I've worked across various industries, including Food, Service, Financial , Energy and Manufacturing. I've taken on a multitude of projects and consistently demonstrated my ability to architect and manage them effectively delivering secure, scalable, and cost-effective solutions. Social Media / Website Links https://www.linkedin.com/in/svenchio/ https://www.techtacofriday.com/ |
Unlocking Fabric Automation: Lessons, Possibilities, and Real-World Insights
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Graphs and Vectors in SurrealDB: Part 2
2024-12-10 · 23:30
Join us for part two of Vector search and Graph use cases in SurrealDB! Learn how you can leverage this functionality in your own projects through informative talks with practical examples. The meetup will highlight:
Attendees will gain practical insights into:
This meetup is ideal for individuals who attended Part 1 or possess a basic understanding of knowledge graph extraction and are eager to learn advanced techniques for improving LLM outputs using graph-based RAG systems. 🗣️ Speaker opportunity - submit your talk! Working on an interesting project that you would like to share with the community? Submit your talk here. ⏰ Date/time: December 10, 6:30 - 9:00PM 📍 Location: The Yard: Columbus Circle Coworking Office Space NYC Agenda 18:30 - 19:00 Welcome drinks, pizza & networking Attendees arrive – grab a drink, explore the space and meet the SurrealDB team. 19:00 - 19:30 Improving LLM Responses with Knowledge Graphs and Semantic Vector Search Sandro Pireno, Director Solutions Engineering at SurrrealDB. Building on the foundational concepts of knowledge graph construction from our last meetup in which we extracted knowledge graphs using a large language model (LLM),, this meetup explores advanced techniques for enhancing LLM outputs using graph-based Retrieval-Augmented Generation (RAG) systems. The session will showcase how integrating structured knowledge from a knowledge graph, coupled with semantic search powered by vector embeddings, can significantly improve the quality and relevance of LLM-generated responses. 19:30 - 20:00 Refreshments & networking Connect with others in the tech community. Grab a slice of pizza & a drink and chat with other attendees and members of the SurrealDB team. 20:00 - 20:30 How Index Uses SurrealDB with Decentralized Autonomous Agents Description: Explore how Index, a decentralized protocol for peer-to-peer discovery, integrates SurrealDB to enhance its network of autonomous agents. Discover how SurrealDB enables dynamic schemas, context-aware indexing, and seamless collaboration between agents. 20:30 - 21:00 Refreshments and networking 21:00 End of event -- Host: Alessandro Pireno \| LinkedIn Alessandro is a seasoned product development and solutions leader with a proven track record of building and scaling data-driven solutions across diverse industries. He has led product strategy and development at companies like HUMAN and Omnicom Media Group, optimized data collection and distribution at GroupM, and was an early leader of success at Snowflake. With a deep understanding of the challenges and opportunities facing today’s tech landscape, Alessandro is passionate about empowering organizations to unlock the full potential of their data through innovative database solutions. Guest speaker: Seref Yarar \| LinkedIn Seref Yarar is the co-founder of Index Network, with 15 years of experience across media, journalism, e-commerce, and ad-tech. His work is shaped by a focus on the semantic web, distributed systems, and decentralized technologies, which influence his approach to information discovery challenges. -- 👉 New to SurrealDB? Get started here. FAQs Is the venue accessible? The Yard is located on the 2nd floor. When you arrive, just let security know that you're heading up to The Yard. Am I guaranteed a ticket at this event? Our events are tech-focused and in the interest of keeping our events relevant and meaningful for those attending, tickets are issued at our discretion. We therefore reserve the right to refund ticket orders before the event and to request proof of identity and/or professional background upon entry. Is this event for me? SurrealDB events are for software engineers, developers, architects, data scientists, data engineers, or any tech professionals keen to discover more about SurrealDB: a scalable multi-model database that allows users and developers to focus on building their applications with ease and speed. Are there any House Rules? At SurrealDB, we are committed to providing live and online events that are safe and enjoyable for all attending. Please review our Code of Conduct and Privacy Policy for more information. It is compulsory for all attendees to be registered with a first and last name in order to attend. Any attendees who do not adhere to these requirements will be refused a ticket. |
Graphs and Vectors in SurrealDB: Part 2
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Zurich dbt Meetup #3 (in-person)
2024-12-05 · 17:45
The 3rd edition of the Switzerland dbt Meetup and the first one in lovely Zurich! 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, data ops, modeling, testing, and team structures. 🤝 Organizer: Astrafy is organizing this event, enabled by the community team at dbt Labs 📍Venue Host: Spaces - Bleicherweg 10, Zurich 🍕 Catering: Pizzas and drinks To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy 🗓️ Agenda
🗣️ 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. Andrea is Head of Data Engineering at Astrafy, with a background in computer science and a strong track record working at various international companies as a data engineer. 🗣️ Presentation #2: On is a fast-growing sports & lifestyle apparel company, headquarted in Zurich. The tech & data landscape at On is no exception to such growth. The Data Platform Team provides a case study on how to balance excellence and foster innovation across this landscape and how dbt is indispensable in achieving a governed data-driven culture. Speaker bio: Lewis Osborne is a Lead Data Engineer at On, passionate about building robust analytics models and user-friendly BI interfaces. With expertise in cloud data engineering and tools like dbt, Looker, and BigQuery, he focuses on creating impactful data solutions and streamlined workflows. EVENT DETAILS: The doors open at 6:45 pm. Presentations begin at 7 pm. Food and refreshments will be provided. Please note: Photos will be taken at the meetup and will be used on Astrafy’s communication channels. Please get in touch ahead of the event if you do not consent to this. ➡️ Join the dbt Slack community: https://www.getdbt.com/community/ 🤝 For the best Meetup experience, make sure to join the #local-\ channel in dbt Slack (https://slack.getdbt.com/). ---------------------------------- dbt is the standard in data transformation, used by over 40,000 organizations worldwide. 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/ |
Zurich dbt Meetup #3 (in-person)
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Please rsvp for the event here: https://www.meetup.com/nlp_london/events/304258590/?utm_medium=referral&utm_campaign=share-btn_savedevents_share_modal&utm_source=link DetailsOverview: If you are using LLMs in applications for the Legal sector, then you may come across the challenges of connecting siloed information, building Assistants to automate tasks, and reliably making sense of the semantics of your data. We’re delighted to have three fantastic presentations sharing actionable insights:
Speaker: Jocelyn Matthews from Pinecone If you had a collection of every kind of animal on earth, from mules to narwhals to goldfish, how would we pick out just the housepets? And how does AI distinguish between a pack mule and a Moscow Mule? Lawyers often need to identify underlying themes or concepts to build arguments, which is akin to how embeddings distil high-dimensional data into lower-dimensional, meaningful representations. Legal professionals are skilled at recognizing fact patterns that may apply across different cases. This is analogous to how embeddings recognize conceptual or contextual similarities in data. Embeddings are numerical representations that capture the essential features and relationships of objects, like words or images, in a continuous vector space, enabling tasks such as semantic search, clustering, and recommendations. We'll explore the core concepts of embeddings, using relatable examples to make advanced ideas accessible. Legal professionals may find embeddings particularly relevant due to their ability to distinguish between different entities and concepts with nuance. Such capability is crucial for addressing legal issues like precedent analysis, data privacy, bias mitigation, intellectual property, contract review and compliance, legal research, risk management in mergers and acquisitions, and automated redaction of sensitive information. By understanding how embeddings distinguish between concepts, attendees can draw parallels to their own legal reasoning processes, gaining insights into how these AI mechanisms intersect with legal frameworks. This session encourages legal professionals to explore the intellectual and professional possibilities that embeddings present, deepening an understanding of AI’s role in law. 2. Topic: TrustGraph: AI Powered Knowledge Graphs Meet Scalable Data Engineering Speaker: Mark Adams from TrustGraph Heavily regulated industries often see regulations as a barrier to innovation. When regulations are coupled with disconnected data silos, innovation grinds to a halt. Critical information is buried in thousands of pages - tens of thousands of pages - of technical designs that must couple to regulatory requirements. Whether it’s legal texts, compliance docs, or even industry best practices, connecting these data silos is essential to enabling technical innovation. In this technical presentation, Mark will provide a brief overview of the TrustGraph open source framework and how it can break down these information barriers and connect the most complex of data silos. The focus will be on demonstrating how TrustGraph deploys reliable, scalable, and accurate AI agents through its modular design and innovative features. Live demo of course! 3. Topic: What are AI assistants and how can they help me and how can I put AI in production? Speakers: Christoffer Noring and Liam Hampton from Microsoft Their talk and demo will cover LLMs, Tool calling, Assistants and also showcase some practical examples where assistants shine. Prepare to be inspired and hopefully get started to create your own AI Assistant. We'll also look into IaC, infrastructure as code, some tooling associated, and how to deploy your apps. By the end of the meetup we will have gained practical, actionable insights and takeaways that can accelerate development and deployment of NLP in production for Legal applications. Schedule 18.00: Doors Open 18:00-18.30: Networking (food, drink) 18.30-20.00: Talks 20.00-21.00: More networking Extra special thanks to our sponsors and speakers:
About the speakers
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Unlocking information in the Legal sector using Assistants, VectorDBs, GraphDBs
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PyDataMCR December
2024-12-02 · 18:30
PyDataMCR December Talks Continuing with some more great talks we are hosted this month by Krakenflex. THE TALKS Beyond Traditional Recommenders: Leveraging Graph Neural Networks for Food Delivery Platforms - Chris Dambamuromo (he/him) In this talk, Chris will demonstrate how to implement a Graph Neural Network (GNN) based recommendation system for food delivery platforms. Drawing from real-world examples, he'll explore how GNNs can effectively balance the complex dynamics of a three-sided marketplace - customers, restaurants, and delivery partners. The presentation will cover practical aspects of working with limited data, implementing GraphSAGE, and handling multiple optimisation objectives. This independent project represents his exploration of recommendation systems and graph-based machine learning, bringing together his technical expertise and interest in practical AI applications. Chris is a Data Engineer with over a decade of software development experience, specialising in data engineering and machine learning. With a background in Applied Mathematics and Intelligent Computer Systems, he's passionate about applying advanced AI techniques to solve real-world problems. Currently focusing on recommendation systems and graph-based machine learning, Chris brings practical insights from his experience in both software development and data engineering. The talk will include code examples and is suitable for data scientists, ML/Data engineers, and anyone interested in practical applications of Graph Neural Networks. Building Seamless Pipelines with DBT and Python - Andy Stafford Hughes (he/him) I will discuss how we successfully automated testing with DBT and Python in our Data Products team. Andrew is an Enterprise Data & AI Test Architect with extensive experience in quality assurance across industries like healthcare, gaming, and retail. Currently at AstraZeneca, he is enhancing test automation frameworks for data science and GenAI projects while mentoring teams to improve their automation testing capabilities globally. LOCATION We'll be at Krakenflex, who are also kindly supplying catering. The capacity is limited to 90. After the talks we'll all head somewhere local for some post-event socialising. EVENT GUIDELINES PyDataMCR is a strictly professional event, as such professional behaviour is expected. PyDataMCR is a chapter of PyData, an educational program of NumFOCUS and thus abides by the NumFOCUS Code of Conduct https://pydata.org/code-of-conduct.html Please take a moment to familiarise yourself with its contents. ACCESSIBILITY Under 16s welcome with a responsible guardian. There is a quiet room available if needed. Toilets are accessible. SPONSORS Thank you to NUMFocus for sponsoring Meetup and further support Thank you to AutoTrader for sponsoring PyDataMCR. Thank you to Krakenflex for sponsoring PyDataMCR, as well as hosting this event! |
PyDataMCR December
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Belgium dbt Meetup #8 (in-person)
2024-11-28 · 17:00
The 8th edition of the Belgium dbt Meetup will take place in Hasselt on November 28th. 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, data ops, modeling, testing, and team structures. 🏠Venue Host & Catering Provider: DataSense, Corda Campus, Kempische Steenweg 309, 3500 Hasselt 🤝Organizers: Sam Debruyn & Arianna Van de Maele 🚆 Getting there: plenty of parking available around the building - Kiewit train station at walking distance (travel planning) To attend, please read the Health and Safety Policy and Terms of Participation: https://www.getdbt.com/legal/health-and-safety-policy Our venue has capacity limits, so please only RSVP if you intend to come. Reach out - send a message in #local-belgium on Slack - if you need to cancel last minute or change your RSVP status on the Meetup to "Not Going." 📝Agenda:
🗣️Presentations: 1) Containerized Deployment for dbt Projects Simplify your dbt workflows Enhance security Scale with ease Guarantee consistent performance Don't miss this opportunity to learn how containerized deployment can revolutionize your dbt projects. by Jim Vekemans, Data Engineer at DataSense Data engineer at DataSense, with multiple large-scale dbt projects implemented at different clients. 2) Maintaining your conventions amid the chaos of dbt As dbt projects continue to grow in size and complexity it is becoming increasingly common for each organisation to have its own conventions, standards and ways-of-working. But how can these conventions be maintained as the dbt project continues to expand? Does chaos reign or can we use our engineering knowledge to maintain our conventions? Let’s examine several approaches to maintaining standards in a dbt project. by Pádraic Slattery, Analytics Engineer at Xebia Data Pádraic has worked across several industries in roles including data analysis, Business Intelligence development and data engineering. He currently works as an Analytics Engineer at Xebia Data, a Data and AI consultancy based in Amsterdam where he helps clients deploy data platforms, onboard business use cases and upskill internal employee's data literacy. We are always looking for speakers! To submit a session for one of the next meetups, please use our Sessionize page. Are you in doubt if you're ready to give a talk? Check out dbt Labs's guide on how to deliver a fantastic presentation! ➡️ Join the dbt Slack community: https://www.getdbt.com/community/ 🤝 For the best Meetup experience, make sure to join the #local-belgium channel in dbt Slack (https://slack.getdbt.com/)! dbt is the standard in data transformation, used by over 40,000 organizations worldwide. 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/ |
Belgium dbt Meetup #8 (in-person)
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