AI Book Club: Vision Language Models (VLMs)
September's book is "Vision Language Models!
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 Flyte MLOps Slack group.
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About the book:
Title: Vision Language Models
Authors: Merve Noyan, Andrés Marafioti, Miquel Farré, Orr Zohar
Published: June 2026
O'rielly: https://learning.oreilly.com/library/view/vision-language-models/9798341624030/
Chapters:
1. Introduction to Vision and Language
2. Vision Language Model Applications
3. Vision Language Model Training
4. Training Data and Preprocessing for VLMs
5. Post-Training Vision Language Models
6. Core Architectures of Vision Language Models
7. Deploying Models for Inference at Scale
8. Document AI
9. Video-Language Models
10. Any-to-Any Models
11. Advanced Topics and Cutting-Edge Research
Book Description
Vision language models (VLMs) combine computer vision and natural language processing to create powerful systems that can interpret, generate, and respond in multimodal contexts. Vision Language Models is a hands-on guide to building real-world VLMs using the most up-to-date stack of machine learning tools from Hugging Face, Meta (PyTorch), NVIDIA (Cuda), and others, written by leading researchers and practitioners Merve Noyan, Miquel Farré, Andrés Marafioti, and Orr Zohar. From image captioning and document understanding to advanced zero-shot inference and retrieval-augmented generation (RAG), this book covers the full VLM application and development lifecycle.
Designed for ML engineers, data scientists, and developers, this guide distills cutting-edge VLM research into practical techniques. Readers will learn how to prepare datasets, select the right architectures, fine-tune and deploy models, and apply them to real-world tasks across a range of industries.
Explore core model architectures and alignment techniques
Train and fine-tune VLMs with Hugging Face, PyTorch, and others
Deploy models for applications like image search and captioning
Implement advanced inference strategies, from zero-shot to agentic systems
Build scalable VLM systems ready for production use