Best books for Machine Learning Engineers

Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 3rd Edition

by Aurélien Géron · 2017

~860 pages~22 hrs to read

Project-driven walk-through of the machine learning workflow, from classical models in Scikit-Learn to neural networks in Keras. Work through it once you are comfortable with Python and basic statistics.

Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong · 2020

~370 pages~9 hrs to read

Covers the linear algebra, calculus, probability and optimisation that underpin machine learning, then applies them to regression, PCA and classification. Read it early if your background is software rather than maths.

Deep Learning with Python, 2nd Edition

by François Chollet · 2017

~400 pages~10 hrs to read

An accessible introduction to deep learning from the creator of Keras, covering computer vision, text and generative models with working code. Read it after you are comfortable with classical machine learning.

Machine Learning Design Patterns

by Valliappa Lakshmanan, Sara Robinson, Michael Munn · 2020

~400 pages~10 hrs to read

Catalogues recurring solutions to data, training and serving problems such as feature stores, checkpoints and workflow pipelines. Useful when you are designing a system rather than just training a model.

Building Machine Learning Powered Applications: Going from Idea to Product

by Emmanuel Ameisen · 2020

~260 pages~7 hrs to read

Follows one ML product from the first idea through modelling, validation, deployment and monitoring. Read it when you are building your first end-to-end application.

Deep Learning with PyTorch

by Eli Stevens, Luca Antiga, Thomas Viehmann · 2020

~520 pages~13 hrs to read

Teaches PyTorch from tensors and autograd through training loops, data loading and deploying a model. A solid choice when your team trains and serves its own networks.

Machine Learning Engineering in Action

by Ben Wilson · 2022

~300 pages~8 hrs to read

Focuses on the engineering habits that get ML projects delivered: planning, experimentation, code quality, testing and release. Read it when you are moving from prototypes to code a team can maintain.

Hands-On Large Language Models: Language Understanding and Generation

by Jay Alammar, Maarten Grootendorst · 2024

~400 pages~10 hrs to read

Visual, code-first coverage of tokenisation, embeddings, prompting, retrieval and fine-tuning for large language models. Read it when your role starts to involve building with LLMs.

Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

by Chip Huyen · 2022

~390 pages~10 hrs to read

Covers data pipelines, deployment, monitoring and the lifecycle of production models. Read it when your work shifts from notebooks to models that other teams depend on.

AI Engineering: Building Applications with Foundation Models

by Chip Huyen · 2025

~350 pages~9 hrs to read

Covers evaluation, prompt engineering, RAG, fine-tuning, inference optimisation and the architecture of applications built on foundation models. Aimed at engineers who own those systems and decide how they are evaluated and run.

Reliable Machine Learning: Applying SRE Principles to ML in Production

by Cathy Chen, Niall Murphy, Kranti Parisa, D. Sculley, Todd Underwood · 2022

~410 pages~10 hrs to read

Applies site reliability practice to ML: service levels, incident response, monitoring, data and model lifecycle, and organisational design. Read it when you are accountable for how models behave in production.

Designing Data-Intensive Applications

by Martin Kleppmann · 2017

~620 pages~16 hrs to read

Explains storage engines, replication, partitioning, transactions and stream and batch processing, and the trade-offs between them. Read it when you design the systems that data and models run on.