AI Builders and Learners SF

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: LLMOps
Authors: Abi Aryan
Published: July 2025

https://learning.oreilly.com/library/view/llmops/9781098154196/

Chapters:
1. Introduction to Large Language Models
2. Introduction to LLMOps
3. LLM-Based Applications
4. Data Engineering for LLMs
5. Model Domain Adaptation for LLM-Based Applications
6. API-First LLM Deployment
7. Evaluation for LLMs
8. Governance: Monitoring, Privacy, and Security
9. Scaling: Hardware, Infrastructure, and Resource Management
10. The Future of LLMs and LLMOps

Book Description
Here's the thing about large language models: they don't play by the old rules. Traditional MLOps completely falls apart when you're dealing with GenAI. The model hallucinates, security assumptions crumble, monitoring breaks, and agents can't operate. Suddenly you're in uncharted territory. That's exactly why LLMOps has emerged as its own discipline.
LLMOps: Managing Large Language Models in Production is your guide to actually running these systems when real users and real money are on the line. This book isn't about building cool demos. It's about keeping LLM systems running smoothly in the real world.

• Navigate the new roles and processes that LLM operations require
• Monitor LLM performance when traditional metrics don't tell the whole story
• Set up evaluations, governance, and security audits that actually matter for GenAI
• Wrangle the operational mess of agents, RAG systems, and evolving prompts
• Scale infrastructure without burning through your compute budget

https://learning.oreilly.com/library/view/llmops/9781098154196/

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: Deep Learning for Biology
Authors: Charles Ravarani, Natasha Latysheva
Published: July 2025

https://learning.oreilly.com/library/view/deep-learning-for/9781098168025/

Chapters:
1. Introduction
2. Learning the Language of Proteins
3. Learning the Logic of DNA
4. Understanding Drug–Drug Interactions Using Graphs
5. Detecting Skin Cancer in Medical Images
6. Learning Spatial Organization Patterns Within Cells
7. Tips and Tricks for Deep Learning in Biology

Book Description
Bridge the gap between modern machine learning and real-world biology with this practical, project-driven guide. Whether your background is in biology, software engineering, or data science, Deep Learning for Biology gives you the tools to develop deep learning models for tackling a wide range of biological problems.
Authors Charles Ravarani and Natasha Latysheva guide you through hands-on projects applying deep learning to domains like DNA, proteins, biological networks, medical images, and microscopy. Each chapter is a self-contained mini-project, with step-by-step explanations that teach you how to train and interpret deep learning models using real biological data.

• Build models for real-world biological problems such as gene regulation, protein function prediction, drug interactions, and cancer detection
• Apply architectures like convolutional neural networks, transformers, graph neural networks, and autoencoders
• Use Python and interactive notebooks for hands-on learning
• Build problem-solving intuition that generalizes beyond biology

Whether you’re exploring new methods, transitioning into computational biology, or looking to make sense of machine learning in your field, this book offers a clear and approachable path forward.

https://learning.oreilly.com/library/view/deep-learning-for/9781098168025/

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/