SF AI, Machine Learning and Computer Vision Meetup
Nov 20, 2024 | 5:30 to 8:00 PM PT
Register for the event at GitHub's offices in San Francisco. RSVPs are limited!
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Date and Time
Nov 20, 2024 from 5:30 PM to 8:00 PM Pacific
Location
The Meetup will take place at GitHub’s offices in San Francisco. Note that pre-registration is mandatory.
88 Colin P Kelly Jr St, San Francisco, CA 94107
Hands-On with Meta AI's CoTracker3: Parsing and Visualizing Point Tracking Output
Harpreet Sahota
Voxel51
In this presentation we’ll explore Meta AI’s CoTracker3, a state-of-the-art point tracking model that effectively leverages real-world videos during training. He dives into the practical aspects of running inference with CoTracker3 and parsing its output into FiftyOne, a powerful open-source tool for dataset curation, analysis, and visualization. Through a hands-on demonstration, Harpreet shows how to prepare a video for inference, run the model, examine its output, and parse the model’s output into FiftyOne’s keypoint format for seamless integration and visualization within the FiftyOne app.
About the Speaker
Harpreet Sahota is a hacker-in-residence and machine learning engineer with a passion for deep learning and generative AI. He’s got a deep interest in RAG, Agents, and Multimodal AI.
Why Speed Matters in Compound AI Systems: Making Models Go Vroom at Fireworks
Mikiko Bazeley
Fireworks.ai
In this talk, we will analyze the critical role of speed in compound AI systems and how it impacts overall performance and user experience. Attendees will learn about some of the strategies we’ve implemented at Fireworks to optimize model efficiency, reduce latency, and enhance responsiveness.
About the Speaker
Mikiko Bazeley is a Developer Relations Engineer at Fireworks.ai, specializing in MLOps and data science. With a passion for building high-performance AI systems, she collaborates with leading companies to drive innovation in generative AI. Mikiko loves empowering developers through hands-on workshops and engaging content, making complex AI concepts accessible and exciting.
DIY LLMs
Charles Frye
Modal Labs
In this talk, Charles will give a guided tour through the components of a self-hosted LLM service, from hardware considerations to engineering tools like ‘evals,’ all the way to the application layer. We’ll consider the open weights models, open source software, and infrastructure that power LLM applications. He will heavily shill the open source vLLM project.
About the Speaker
Charles Frye builds applications of neural networks at Modal. He got his PhD at Berkeley for work on neural network optimization. He previously worked at Weights & Biases and Full Stack Deep Learning.
Find a Meetup Near You
Join the AI and ML enthusiasts who have already become members
The goal of the AI, Machine Learning, and Computer Vision Meetup network is to bring together a community of data scientists, machine learning engineers, and open source enthusiasts who want to share and expand their knowledge of AI and complementary technologies. If that’s you, we invite you to join the Meetup closest to your timezone.
