5 Books to Move from Software Engineer to AI/ML Engineer

Bashiri Smith · Facebook reel · 2026-09-20 · 0:13 · 32,054 views · Open on Facebook

Topics: Programming & ML Foundations, Start Here: Roadmaps & Strategy, LLMOps, Deployment & Monitoring · Level: beginner

Summary

A short reel listing five books for software engineers moving into AI/ML engineering. The list goes in order from math foundations, to hands-on classic ML, to deep learning theory, to building apps on foundation models, and ends with LLMOps for running models in production. The creator links to a Google Doc with the books but notes he couldn't find PDFs for all of them.

Key points

• Start with the math: 'Mathematics for Machine Learning' covers linear algebra, calculus, probability and optimization for ML.
• Build practical ML skills with 'Hands-On Machine Learning with Scikit-Learn and PyTorch' (classic ML, then neural networks in code).
• Go deeper on theory with 'Deep Learning' (Goodfellow, Bengio, Courville).
• Learn to build applications on LLMs and other foundation models with 'AI Engineering: Building Applications with Foundation Models' (Chip Huyen).
• Finish with production skills from 'LLMOps: Managing Large Language Models in Production'.
• The order runs from foundations to applied AI engineering to production operations, so it works as a reading roadmap.
• The creator's Google Doc has links to the books, but not every book has a PDF.

Resources mentioned

• Mathematics for Machine Learning · book · mml-book.github.io · free
  Book by Deisenroth, Faisal and Ong covering the linear algebra, calculus, probability and optimization behind ML.
• Hands-On Machine Learning with Scikit-Learn and PyTorch · book · homl.info · paid
  Aurelien Geron's hands-on ML book; the official site has the notebooks plus free online chapters and appendices.
• Deep Learning · book · deeplearningbook.org · free
  Strengthens your conceptual understanding as an AI engineer, and is essential for an ML engineer who wants to go far.
• AI Engineering: Building Applications with Foundation Models (Chip Huyen) · book · github.com · paid
  Essential for AI engineers, even if some topics may not cover the newest tools. Good to know for ML engineers who want to build products with their models.
  Also in: AI Engineer Roadmap Before 2027: Fundamentals, RAG, Agents, Ops, Evals (Bashiri Smith on Facebook · notes), AI Engineer Roadmap: Fundamentals, RAG, Agents, Books & Your First AI Service (Bashiri Smith on Facebook · notes)
• LLMOps: Managing Large Language Models in Production · book · oreilly.com · paid · open in a browser to verify
  Abi Aryan's O'Reilly book on running LLM applications in production (paid). The guide linked an unofficial PDF copy; this is the publisher's page.

From the PDF shared here: 5 books to upgrade from software engineer into Ai/ML engineer

Open the original · 1 pages

A one-page reading list of five books for software engineers moving into AI or ML engineering. It covers math (Mathematics for Machine Learning), classic ML (Hands-On ML), deep learning (Goodfellow et al.), building AI products (Chip Huyen's AI Engineering) and LLMOps (Abi Aryan). Each book comes with a short note on whether it is essential for AI engineers, ML engineers or both. The page ends with a pitch for the creator's paid Skool community.

• AI Engineering: Building Applications with Foundation Models (Chip Huyen) · book · github.com · paid
  Essential for AI engineers, even if some topics may not cover the newest tools. Good to know for ML engineers who want to build products with their models.
  Also in: AI Engineer Roadmap Before 2027: Fundamentals, RAG, Agents, Ops, Evals (Bashiri Smith on Facebook · notes), AI Engineer Roadmap: Fundamentals, RAG, Agents, Books & Your First AI Service (Bashiri Smith on Facebook · notes)
• Deep Learning · book · deeplearningbook.org · free
  Strengthens your conceptual understanding as an AI engineer, and is essential for an ML engineer who wants to go far.
• LLMOps: Managing Large Language Models in Production · book · oreilly.com · paid · open in a browser to verify
  Abi Aryan's O'Reilly book on running LLM applications in production (paid). The guide linked an unofficial PDF copy; this is the publisher's page.
• Hands-On Machine Learning with Scikit-Learn and PyTorch (Aurélien Géron) · book · ageron.github.io · check price
  Hands-on guide to ML techniques. A basic grasp of them helps AI engineers and is essential for ML engineers. The link only goes to the free Appendix E PDF, not the full book.
• Mathematics for Machine Learning (Deisenroth, Faisal, Ong) · book · mml-book.github.io · free
  Math for ML. You don't strictly need it to be an AI engineer, though it is very useful, but you do need math to be an ML engineer.

Try this

• Open the creator's Google Doc to get the book links.
• Read the books in order: math, hands-on ML, deep learning, AI engineering, LLMOps.
• Comment 'Books' on the reel to get the links sent to you.