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Important: register on the AICamp event website is required for admission.

Description: Welcome to the GenAI meetup in New York City. Join us for deep dive tech talks on AI, GenAI, LLMs and Agent, hands-on experiences on code labs, workshops, and networking with speakers and fellow developers.

Agenda: * 5:30pm\~6:00pm: Checkin, Food and networking * 6:00pm\~6:10pm: Welcome/community update * 6:10pm\~8:00pm: Tech talks * 8:00pm: Q&A, Open discussion

Speakers and Topics: Check the event website for Speakers and Topics. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsor. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in New York and 500K+ worldwide.

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers. *

Important: register on the AICamp event website is required for admission.

Description: Welcome to the GenAI meetup in New York City. Join us for deep dive tech talks on AI, GenAI, LLMs and Agent, hands-on experiences on code labs, workshops, and networking with speakers and fellow developers.

Agenda: * 5:30pm\~6:00pm: Checkin, Food and networking * 6:00pm\~6:10pm: Welcome/community update * 6:10pm\~8:00pm: Tech talks * 8:00pm: Q&A, Open discussion

Speakers and Topics: Check the event website for Speakers and Topics. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsor. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in New York and 500K+ worldwide.

Local and Global AI Community on Discord Join us on discord for local and global AI tech community:

• Events chat: chat and connect with speakers and global and local attendees;
• Learning AI: events, learning materials, study groups;
• Startups: innovation, projects collaborations, founders/co-founders;
• Jobs and Careers: job openings, post resumes, hiring managers. *

Important: Register on the AICamp event website is required for admission.

Welcome to the AI meetup in Berlin, in collaboration with Thoughtworks. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, networking with speakers and fellow developers.

Agenda: * 6:00pm\~7:00pm: Checkin and networking * 7:00pm\~9:00pm: Tech talks and Q&A * 9:00pm: Open discussion and Mixer

Speakers/Topics: Check the event website for speakers and topics. Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 10,000+ AI developers in Berlin and 500K+ worldwide.

Tech talk as part of the AI meetup in Paris. Topics include AI, GenAI, LLMs and Agents.

Tech talk as part of the AI meetup in Paris. Topics include AI, GenAI, LLMs and Agents.

Important: Register on the event website is required for admission.

Welcome to the AI meetup in London. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Speakers/Topics:

• - Pascal Schulze (Firebolt)
• - Daniël van Eeden (PingCAP)

Check the event website for speakers and topics. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in London and 500K+ worldwide.

** Important RSVP here (Due to room capacity and building security, you must pre-register at the link for admission.)

Description: Welcome to our in-person AI meetup in New York. Join us for deep dive tech talks on AI, GenAI, LLMs and ML, hands-on workshops, food/drink, networking with speakers and fellow developers.

Tech Talk: Agentic and Compound AI Done Right Speaker: Rob Cheung (Substrate) Abstract: This talk will cover the ins and outs of running multi-model workloads at scale. Learn about the latest techniques and frameworks to ensure your AI programs run fast and efficiently. Participate in a hands-on workshop that showcases practical applications of the concepts discussed in the talk. Gain insights into optimizing your AI workflows and see real-world examples in action.

Tech Talk: AI-Driven Insights: Enhancing Product Search and Analytics Speaker: Justin Strnatko (SingleStore) Abstract: Learn how AI can transform product search and streamline data analysis. This presentation will demonstrate the power of AI-driven insights to improve efficiency and support informed decision-making.

Tech Talk: Human Pose Estimation in Real-Time Utilizing Edge AI Accelerated Hardware Speaker: Tim Spann (Zilliz) Abstract: Utilizing a Hailo AI Acceleration Module with a Raspberry Pi 5 device we will process real-time video streams from an edge camera and store real-time results in a vector database and send messages to Slack channels. We will show you how to build Edge AI applications that can stream unstructured data to the cloud or store it locally in a local Vector database. We will run a live demo of a neural network inference accelerator capable of 13 tera-operations per second (TOPS).

Speakers/Topics: Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Sponsors: We are actively seeking sponsors to support our community. Whether it is by offering venue spaces, providing food/drink, or cash sponsor. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 20,000+ AI developers in New York or 350K+ worldwide.

Community on Slack/Discord

• Event chat: chat and connect with speakers and attendees
• Sharing blogs, events, job openings, projects collaborations

When August 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-aug-8-2024/

GenAI for Video: Diffusion-Based Editing and Generation

Recently, diffusion-based generative AI models have gained popularity due to their wide applications in the image domain. Additionally, there is growing attention to the video domain because of its ubiquitous presence in real-world applications. In this talk, we will discuss the future of GenAI in the video domain, highlighting recent advancements and exploring its potential and impact on video editing and generation. We will also examine the challenges and opportunities these technologies present, offering insights into how they can revolutionize the video industry.

About the Speaker

Ozgur Kara is a PhD student in the Computer Science Department at the University of Illinois at Urbana-Champaign. He earned his Bachelor’s degree in Electrical and Electronics Engineering from Boğaziçi University. His research focuses on generative AI and computer vision, particularly on generative AI and its applications in video.

Evaluating RAG Models for LLMs: Key Metrics and Frameworks

Evaluating the model performance is the key for ensuring effectiveness and reliability of LLM models. In this talk, we will look into the intricate world of RAG evaluation metrics and frameworks, exploring the various approaches to assessing model performance. We will discuss key metrics such as relevance, diversity, coherence, and truthfulness and examine various evaluation frameworks, ranging from traditional benchmarks to domain-specific assessments, highlighting their strengths, limitations, and potential implications for real-world applications.

About the Speaker

Abi Aryan is the founder of Abide AI and a machine learning engineer with over eight years of experience in the ML industry building and deploying machine learning models in production for recommender systems, computer vision, and natural language processing—within a wide range of industries such as ecommerce, insurance, and media and entertainment. Previously, she was a visiting research scholar at the Cognitive Sciences Lab at UCLA where she worked on developing intelligent agents. Also, she has authored research papers on AutoML, multi agent systems, and LLM cost modeling and evaluations and is currently authoring LLMOps: Managing Large Language Models in Production for O'Reilly Publications.

Why You Should Evaluate Your End-to-End LLM applications with In-House Data

This task discusses end-to-end NLP evaluations, focusing on key areas, common pitfalls, and the workings of production evaluation systems. It also explores how to fine-tune in-house LLMs as judges using custom data for more accurate performance assessments.

About the Speaker

Mahesh Deshwal is a Data Scientist and AI researcher with over 5.5 years of experience in using ML and AI to solve business problems, particularly in Computer Vision, NLP, recommendation, and personalization. As the author of the paper PHUDGE and an active open source contributor, he excels in delivering end-to-end solutions, from user requirements to deploying scalable models using MLOps.

When August 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-aug-8-2024/

GenAI for Video: Diffusion-Based Editing and Generation

Recently, diffusion-based generative AI models have gained popularity due to their wide applications in the image domain. Additionally, there is growing attention to the video domain because of its ubiquitous presence in real-world applications. In this talk, we will discuss the future of GenAI in the video domain, highlighting recent advancements and exploring its potential and impact on video editing and generation. We will also examine the challenges and opportunities these technologies present, offering insights into how they can revolutionize the video industry.

About the Speaker

Ozgur Kara is a PhD student in the Computer Science Department at the University of Illinois at Urbana-Champaign. He earned his Bachelor’s degree in Electrical and Electronics Engineering from Boğaziçi University. His research focuses on generative AI and computer vision, particularly on generative AI and its applications in video.

Evaluating RAG Models for LLMs: Key Metrics and Frameworks

Evaluating the model performance is the key for ensuring effectiveness and reliability of LLM models. In this talk, we will look into the intricate world of RAG evaluation metrics and frameworks, exploring the various approaches to assessing model performance. We will discuss key metrics such as relevance, diversity, coherence, and truthfulness and examine various evaluation frameworks, ranging from traditional benchmarks to domain-specific assessments, highlighting their strengths, limitations, and potential implications for real-world applications.

About the Speaker

Abi Aryan is the founder of Abide AI and a machine learning engineer with over eight years of experience in the ML industry building and deploying machine learning models in production for recommender systems, computer vision, and natural language processing—within a wide range of industries such as ecommerce, insurance, and media and entertainment. Previously, she was a visiting research scholar at the Cognitive Sciences Lab at UCLA where she worked on developing intelligent agents. Also, she has authored research papers on AutoML, multi agent systems, and LLM cost modeling and evaluations and is currently authoring LLMOps: Managing Large Language Models in Production for O'Reilly Publications.

Why You Should Evaluate Your End-to-End LLM applications with In-House Data

This task discusses end-to-end NLP evaluations, focusing on key areas, common pitfalls, and the workings of production evaluation systems. It also explores how to fine-tune in-house LLMs as judges using custom data for more accurate performance assessments.

About the Speaker

Mahesh Deshwal is a Data Scientist and AI researcher with over 5.5 years of experience in using ML and AI to solve business problems, particularly in Computer Vision, NLP, recommendation, and personalization. As the author of the paper PHUDGE and an active open source contributor, he excels in delivering end-to-end solutions, from user requirements to deploying scalable models using MLOps.

When August 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-aug-8-2024/

GenAI for Video: Diffusion-Based Editing and Generation

Recently, diffusion-based generative AI models have gained popularity due to their wide applications in the image domain. Additionally, there is growing attention to the video domain because of its ubiquitous presence in real-world applications. In this talk, we will discuss the future of GenAI in the video domain, highlighting recent advancements and exploring its potential and impact on video editing and generation. We will also examine the challenges and opportunities these technologies present, offering insights into how they can revolutionize the video industry.

About the Speaker

Ozgur Kara is a PhD student in the Computer Science Department at the University of Illinois at Urbana-Champaign. He earned his Bachelor’s degree in Electrical and Electronics Engineering from Boğaziçi University. His research focuses on generative AI and computer vision, particularly on generative AI and its applications in video.

Evaluating RAG Models for LLMs: Key Metrics and Frameworks

Evaluating the model performance is the key for ensuring effectiveness and reliability of LLM models. In this talk, we will look into the intricate world of RAG evaluation metrics and frameworks, exploring the various approaches to assessing model performance. We will discuss key metrics such as relevance, diversity, coherence, and truthfulness and examine various evaluation frameworks, ranging from traditional benchmarks to domain-specific assessments, highlighting their strengths, limitations, and potential implications for real-world applications.

About the Speaker

Abi Aryan is the founder of Abide AI and a machine learning engineer with over eight years of experience in the ML industry building and deploying machine learning models in production for recommender systems, computer vision, and natural language processing—within a wide range of industries such as ecommerce, insurance, and media and entertainment. Previously, she was a visiting research scholar at the Cognitive Sciences Lab at UCLA where she worked on developing intelligent agents. Also, she has authored research papers on AutoML, multi agent systems, and LLM cost modeling and evaluations and is currently authoring LLMOps: Managing Large Language Models in Production for O'Reilly Publications.

Why You Should Evaluate Your End-to-End LLM applications with In-House Data

This task discusses end-to-end NLP evaluations, focusing on key areas, common pitfalls, and the workings of production evaluation systems. It also explores how to fine-tune in-house LLMs as judges using custom data for more accurate performance assessments.

About the Speaker

Mahesh Deshwal is a Data Scientist and AI researcher with over 5.5 years of experience in using ML and AI to solve business problems, particularly in Computer Vision, NLP, recommendation, and personalization. As the author of the paper PHUDGE and an active open source contributor, he excels in delivering end-to-end solutions, from user requirements to deploying scalable models using MLOps.

When August 8, 2024 – 10:00 AM Pacific / 1:00 PM Eastern

Where Virtual

Register for the Zoom: https://voxel51.com/computer-vision-events/ai-machine-learning-computer-vision-meetup-aug-8-2024/

GenAI for Video: Diffusion-Based Editing and Generation

Recently, diffusion-based generative AI models have gained popularity due to their wide applications in the image domain. Additionally, there is growing attention to the video domain because of its ubiquitous presence in real-world applications. In this talk, we will discuss the future of GenAI in the video domain, highlighting recent advancements and exploring its potential and impact on video editing and generation. We will also examine the challenges and opportunities these technologies present, offering insights into how they can revolutionize the video industry.

About the Speaker

Ozgur Kara is a PhD student in the Computer Science Department at the University of Illinois at Urbana-Champaign. He earned his Bachelor’s degree in Electrical and Electronics Engineering from Boğaziçi University. His research focuses on generative AI and computer vision, particularly on generative AI and its applications in video.

Evaluating RAG Models for LLMs: Key Metrics and Frameworks

Evaluating the model performance is the key for ensuring effectiveness and reliability of LLM models. In this talk, we will look into the intricate world of RAG evaluation metrics and frameworks, exploring the various approaches to assessing model performance. We will discuss key metrics such as relevance, diversity, coherence, and truthfulness and examine various evaluation frameworks, ranging from traditional benchmarks to domain-specific assessments, highlighting their strengths, limitations, and potential implications for real-world applications.

About the Speaker

Abi Aryan is the founder of Abide AI and a machine learning engineer with over eight years of experience in the ML industry building and deploying machine learning models in production for recommender systems, computer vision, and natural language processing—within a wide range of industries such as ecommerce, insurance, and media and entertainment. Previously, she was a visiting research scholar at the Cognitive Sciences Lab at UCLA where she worked on developing intelligent agents. Also, she has authored research papers on AutoML, multi agent systems, and LLM cost modeling and evaluations and is currently authoring LLMOps: Managing Large Language Models in Production for O'Reilly Publications.

Why You Should Evaluate Your End-to-End LLM applications with In-House Data

This task discusses end-to-end NLP evaluations, focusing on key areas, common pitfalls, and the workings of production evaluation systems. It also explores how to fine-tune in-house LLMs as judges using custom data for more accurate performance assessments.

About the Speaker

Mahesh Deshwal is a Data Scientist and AI researcher with over 5.5 years of experience in using ML and AI to solve business problems, particularly in Computer Vision, NLP, recommendation, and personalization. As the author of the paper PHUDGE and an active open source contributor, he excels in delivering end-to-end solutions, from user requirements to deploying scalable models using MLOps.