AI Builders and Learners - New York

Decembers's book is "Building Applications with AI Agents"!

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: Building Applications with AI Agents
Authors: Michael Albada
Published: September 2025

https://learning.oreilly.com/library/view/building-applications-with/9781098176495/

Chapters:
1. Introduction to Agents
2. Designing Agent Systems
3. User Experience Design for Agentic Systems
4. Tool Use
5. Orchestration
6. Knowledge and Memory
7. Learning in Agentic Systems
8. From One Agent to Many
9. Validation and Measurement
10. Monitoring in Production
11. Improvement Loops
12. Protecting Agentic Systems
13. Human-Agent Collaboration

Book Description
Generative AI has revolutionized how organizations tackle problems, accelerating the journey from concept to prototype to solution. As the models become increasingly capable, we have witnessed a new design pattern emerge: AI agents. By combining tools, knowledge, memory, and learning with advanced foundation models, we can now sequence multiple model inferences together to solve ambiguous and difficult problems. From coding agents to research agents to analyst agents and more, we've already seen agents accelerate teams and organizations. While these agents enhance efficiency, they often require extensive planning, drafting, and revising to complete complex tasks, and deploying them remains a challenge for many organizations, especially as technology and research rapidly develops.
This book is your indispensable guide through this intricate and fast-moving landscape. Author Michael Albada provides a practical and research-based approach to designing and implementing single- and multiagent systems. It simplifies the complexities and equips you with the tools to move from concept to solution efficiently.

• Understand the distinct features of foundation model-enabled AI agents
• Discover the core components and design principles of AI agents
• Explore design trade-offs and implement effective multiagent systems
• Design and deploy tailored AI solutions, enhancing efficiency and innovation in your field

https://learning.oreilly.com/library/view/building-applications-with/9781098176495/

January's book is "Hands-On Machine Learning with Scikit-Learn and PyTorch"!

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: BHands-On Machine Learning with Scikit-Learn and PyTorch
Authors: Aurélien Géron
Published: October 2025

https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/

Chapters:
1. The Machine Learning Landscape
2. End-to-End Machine Learning Project
3. Classification
4. Training Models
5. Decision Trees
6. Ensemble Learning and Random Forests
7. Dimensionality Reduction
8. Unsupervised Learning Techniques
II. Neural Networks and Deep Learning
9. Introduction to Artificial Neural Networks
10. Building Neural Networks with PyTorch
11. Training Deep Neural Networks
12. Deep Computer Vision Using Convolutional Neural Networks
13. Processing Sequences Using RNNs and CNNs
14. Natural Language Processing with RNNs and Attention
15. Transformers for Natural Language Processing and Chatbots
16. Vision and Multimodal Transformers
17. Speeding Up Transformers
18. Autoencoders, GANs, and Diffusion Models
19. Reinforcement Learning
A. Autodiff
B. Mixed Precision and Quantization

Book Description
The potential of machine learning today is extraordinary, yet many aspiring developers and tech professionals find themselves daunted by its complexity. Whether you're looking to enhance your skill set and apply machine learning to real-world projects or are simply curious about how AI systems function, this book is your jumping-off place.
With an approachable yet deeply informative style, author Aurélien Géron delivers the ultimate introductory guide to machine learning and deep learning. Drawing on the Hugging Face ecosystem, with a focus on clear explanations and real-world examples, the book takes you through cutting-edge tools like Scikit-Learn and PyTorch—from basic regression techniques to advanced neural networks. Whether you're a student, professional, or hobbyist, you'll gain the skills to build intelligent systems.

• Understand ML basics, including concepts like overfitting and hyperparameter tuning
• Complete an end-to-end ML project using scikit-Learn, covering everything from data exploration to model evaluation
• Learn techniques for unsupervised learning, such as clustering and anomaly detection
• Build advanced architectures like transformers and diffusion models with PyTorch
• Harness the power of pretrained models—including LLMs—and learn to fine-tune them
• Train autonomous agents using reinforcement learning

https://learning.oreilly.com/library/view/hands-on-machine-learning/9798341607972/

February'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 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/

March'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 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

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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/