Building AI Together - San Francisco

• ​Intro to MCP (Model Context protocol)
• ​The official MCP Python SDK
• ​Flyte 2.0 for serving
• ​How to integrate MCP into your local AI IDE
• ​Security and production configuration: sandboxing and statelessness

​We’ll be building with the Flyte 2.0 SDK and I’ll show what that platform looks like, but you’ll be able to run the agents locally with or without Flyte cluster access.
​Hosted by Niels Bantilan - Chief Machine Learning Engineer Union.ai

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 2025

https://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 Clusters

Book 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/

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 2025

https://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/