About us
Welcome to our AI Meetup! We are a passionate community dedicated to building and learning about artificial intelligence. Whether you're an expert or just starting out, join us to share knowledge, collaborate on projects, and explore the fascinating world of AI together.
We'll be getting different events off the ground, both locally (Seattle) and virtually.
I'd like to have an AI book club going again in 2024, so if you have recommendations for us to read, let us know!
We'll AI cover topics such as Machine Learning (ML), Large Language Models (LLMs), Deep Learning, Data engineering, MLOps, Python, Computer Vision, Natural Language Processing (NLP), the Latest AI developments, and more!
Questions? Reach out to Sage Elliott on LinkedIn: https://www.linkedin.com/in/sageelliott/
Upcoming events
3
Seattle AI, ML and Computer Vision Meetup
Bellevue, WA, USJoin us to hear talks from experts on cutting-edge topics across AI, ML, and computer vision!
REGISTER HERE TO ATTEND
https://voxel51.com/events/seattle-ai-ml-and-computer-vision-meetup-february-12-2026Time and Location
Feb 12, 2026
5:30 - 8:30 PMOfficially register for location details:
https://voxel51.com/events/seattle-ai-ml-and-computer-vision-meetup-february-12-2026Talks:
ALARM: Automated MLLM-Based Anomaly Detection in Complex-EnviRonment Monitoring with Uncertainty Quantification
In the complex environments, the anomalies are sometimes highly contextual and also ambiguous, and thereby, uncertainty quantification (UQ) is a crucial capacity for a multi-modal LLM (MLLM)-based video anomaly detection (VAD) system to succeed. In this talk, I will introduce our UQ-supported MLLM-based VAD framework called ALARM. ALARM integrates UQ with quality-assurance techniques like reasoning chain, self-reflection, and MLLM ensemble for robust and accurate performance and is designed based on a rigorous probabilistic inference pipeline and computational process.
About the Speaker
Congjing Zhang is a third-year Ph.D. student in the Department of Industrial and Systems Engineering at the University of Washington, advised by Prof. Shuai Huang. She is a recipient of the 2025-2027 Amazon AI Ph.D. Fellowship. Her research interests center on large language models (LLMs) and machine learning, with a focus on uncertainty quantification, anomaly detection and synthetic data generation.
The World of World Models: How the New Generation of AI Is Reshaping Robotics and Autonomous Vehicles
World Models are emerging as the defining paradigm for the next decade of robotics and autonomous systems. Instead of depending on handcrafted perception stacks or rigid planning pipelines, modern world models learn a unified representation of an environment—geometry, dynamics, semantics, and agent behavior—and use that understanding to predict, plan, and act. This talk will break down why the field is shifting toward these holistic models, what new capabilities they unlock, and how they are already transforming AV and robotics research.
We then connect these advances to the Physical AI Workbench, a practical foundation for teams who want to build, validate, and iterate on world-model-driven pipelines. The Workbench standardizes data quality, reconstruction, and enrichment workflows so that teams can trust their sensor data, generate high-fidelity world representations, and feed consistent inputs into next-generation predictive and generative models. Together, world models and the Physical AI Workbench represent a new, more scalable path forward—one where robots and AVs can learn, simulate, and reason about the world through shared, high-quality physical context.
About the Speaker
Daniel Gural leads technical partnerships at Voxel51, where he’s building the Physical AI Workbench, a platform that connects real-world sensor data with realistic simulation to help engineers better understand, validate, and improve their perception systems.
Modern Orchestration for Durable AI Pipelines and Agents - Flyte 2.0
In this talk we’ll discuss how the orchestration space is evolving with the current AI landscape, and provide a peak at Flyte 2.0, which makes truly dynamic, compute aware, and durable AI orchestration easy for any type of AI application, from computer vision, agents, and more!
Flyte, the open source orchestration platform, is already being used by thousands of teams to build their AI pipelines. In-fact it’s extremely likely you’ve interacted with AI models trained on Flyte, while on social media, listening to music on using self driving technologies.
About the Speaker
Sage Elliott is an AI Engineer at Union.ai (core maintainers of Flyte).
Context Engineering for Video Intelligence: Beyond Model Scale to Real-World Impact
Video streams combine vision, audio, time-series and semantics at a scale and complexity unlike text alone. At TwelveLabs, we’ve found that tackling this challenge doesn’t start with ever-bigger models — it starts with engineering the right context. In this session, we’ll walk engineers and infrastructure leads through how to build production-grade video AI by systematically designing what information the model receives, how it's selected, compressed, and isolated. You’ll learn our four pillars of video context engineering (Write, Select, Compress, Isolate), see how our foundation models (Marengo & Pegasus) and agent product (Jockey) use them, and review real-world outcomes in media, public-safety and advertising pipelines.
We’ll also dive into how you measure context effectiveness — tokens per minute, retrieval hit rates, versioned context pipelines — and how this insight drives cost, latency and trust improvements. If you’re deploying AI video solutions in the wild, you’ll leave with a blueprint for turning raw video into deployable insight — not by model size alone, but by targeted context engineering.
About the Speaker
James Le currently leads the developer experience function at TwelveLabs - a startup building foundation models for video understanding. He previously operated in the MLOps space and ran a blog/podcast on the Data & AI infrastructure ecosystem.
Build Reliable AI apps with Observability, Validations and Evaluations
As generative AI moves from experimentation to enterprise deployment, reliability becomes critical. This session outlines a strategic approach to building robust AI apps using Monocle for observability and the VS Code Extension for diagnostics, and bug fixing. Discover how to create AI systems that are not only innovative but also predictable and trustworthy.
About the Speaker
Hoc Phan has 20+ years of experience driving innovation at Microsoft, Amazon, Dell, and startups. In 2025, he joined Okahu to lead product and pre-sales, focusing on AI observability and LLM performance. Previously, he helped shape Microsoft Purview via the BlueTalon acquisition and led R&D in cybersecurity and data governance. Hoc is a frequent speaker and author of three books on mobile development and IoT.
12 attendees
AI Book Club: AI Systems Performance Engineering
·OnlineOnlineFebruary's book is "AI Systems Performance Engineering"!
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/
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About the book:
Title: AI Systems Performance Engineering
Authors: Chris Fregly
Published: November 2025https://learning.oreilly.com/library/view/ai-systems-performance/9798341627772/
Chapters:
1. Introduction and AI System Overview
2. AI System Hardware Overview
3. OS, Docker, and Kubernetes Tuning for GPU-based Environments
4. Tuning Distributed Networking Communication
5. GPU-Based Storage I/O Optimizations
6. GPU Architecture, CUDA Programming, and Maximizing Occupancy
7. Profiling and Tuning GPU Memory Access Patterns
8. Occupancy Tuning, Warp Efficiency, and Instruction-Level Parallelism
9. Increasing CUDA Kernel Efficiency and Arithmetic Intensity
10. Intra-Kernel Pipelining, Warp Specialization, and Cooperative Thread Block Clusters
11. Inter-Kernel Pipelining, Synchronization, and CUDA Stream-Ordered Memory Allocations
12. Dynamic Scheduling, CUDA Graphs, and Device-Initiated Kernel Orchestration
13. Profiling, Tuning, and Scaling PyTorch
14. PyTorch Compiler, OpenAI Triton, and XLA Backends
15. Multinode Inference, Parallelism, Decoding, and Routing Optimizations
16. Profiling, Debugging, and Tuning Inference at Scale
17. Scaling Disaggregated Prefill and Decode for Inference
18. Advanced Prefill-Decode and KV Cache Tuning
19. Dynamic and Adaptive Inference Engine Optimizations
20. AI-Assisted Performance Optimizations and Scaling Toward Multimillion GPU ClustersBook Description
Elevate your AI system performance capabilities with this definitive guide to unlocking peak efficiency across every layer of your AI infrastructure. In today's era of ever-growing generative models, AI Systems Performance Engineering equips professionals with actionable strategies to co-optimize hardware, software, and algorithms for high-performance and cost-effective AI systems. Authored by Chris Fregly, a performance-focused engineering and product leader, this comprehensive resource transforms complex systems into streamlined, high-impact AI solutions.
Inside, you'll discover step-by-step methodologies for fine-tuning GPU CUDA kernels, PyTorch-based algorithms, and multinode training and inference systems. You'll also master the art of scaling GPU clusters for high performance, distributed model training jobs, and inference servers.- Codesign and optimize hardware, software, and algorithms to achieve maximum throughput and cost savings
- Implement cutting-edge inference strategies that reduce latency and boost throughput in real-world settings
- Utilize industry-leading scalability tools and frameworks
- Profile, diagnose, and eliminate performance bottlenecks across complex AI pipelines
- Integrate full stack optimization techniques for robust, reliable AI system performance
Whether you're an engineer, researcher, or developer, AI Systems Performance Engineering offers a holistic roadmap for building resilient, scalable, and cost-effective AI systems that excel in both training and inference.
https://learning.oreilly.com/library/view/ai-systems-performance/9798341627772/
4 attendees
AI Book Club: Context Engineering for Multi-Agent Systems
·OnlineOnlineMarch's book is "Context Engineering for Multi-Agent Systems"!
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/
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About the book:
Title: Context Engineering for Multi-Agent Systems
Authors: Denis Rothman
Published: November 2025https://learning.oreilly.com/library/view/context-engineering-for/9781806690053/
Chapters:
Chapter 1: From Prompts to Context: Building the Semantic Blueprint
queue
Chapter 2: Building a Multi-Agent System with MCP
Chapter 3: Building the Context-Aware Multi-Agent System
Chapter 4: Assembling the Context Engine
Chapter 5: Hardening the Context Engine
Chapter 6: Building the Summarizer Agent for Context Reduction
Chapter 7: High-Fidelity RAG and Defense: The NASA-Inspired Research Assistant
Chapter 8: Architecting for Reality: Moderation, Latency, and Policy-Driven AI
Chapter 9: Architecting for Brand and Agility: The Strategic Marketing Engine
Chapter 10: The Blueprint for Production-Ready AI
Chapter 11: Unlock Your Exclusive Benefits####
Book Description
Generative AI is powerful, yet often unpredictable. This guide shows you how to turn that unpredictability into reliability by thinking beyond prompts and approaching AI like an architect. At its core is the Context Engine, a glass-box, multi-agent system you’ll learn to design and apply across real-world scenarios.
Written by an AI guru and author of various cutting-edge AI books, this book takes you on a hands-on journey from the foundations of context design to building a fully operational Context Engine. Instead of relying on brittle prompts that give only simple instructions, you’ll begin with semantic blueprints that map goals and roles with precision, then orchestrate specialized agents using the Model Context Protocol. As the engine evolves, you’ll integrate memory and high-fidelity retrieval with citations, implement safeguards against data poisoning and prompt injection, and enforce moderation to keep outputs aligned with policy. You’ll also harden the system into a resilient architecture, then see it pivot across domains, from legal compliance to strategic marketing, proving its domain independence.By the end of this book, you’ll be equipped with the skills to engineer an adaptable, verifiable architecture you can repurpose across domains and deploy with confidence.
https://learning.oreilly.com/library/view/context-engineering-for/9781806690053/
1 attendee
Past events
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