Top MLops books recommended by experts

At MentorCruise, we are all about making the most out of the experience of others. As part of that, we've connected and asked dozens of experts and professionals about their favourite MLops books – and here are the answers.

Table of Contents

Fundamentals of MLops

Understanding the concepts of MLops starts with understanding the fundamentals. On your way to mastery, it's crucial for you to understand how certain concepts were derived, and why things work like they do. Starting with these resources is the best way to do so.

Practical Mlops: Operationalizing Machine Learning Models

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Recommended by the experts and mentors at MentorCruise

Building Machine Learning Powered Applications: Going from Idea to Product

Learn the skills necessary to design, build, and deploy applications powered by machine learning (ML). Through the course of this hands-on book, you’ll build an example ML-driven application from initial idea to deployed product. ...

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Implementing MLOps in the Enterprise

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. ...

Recommended by the experts and mentors at MentorCruise

Machine Learning Design Patterns

The design patterns in this book capture best practices and solutions to recurring problems in machine learning. The authors, three Google engineers, catalog proven methods to help data scientists tackle common problems throughout the ML process. ...

Recommended by the experts and mentors at MentorCruise

Practical MLOps: Operationalizing Machine Learning Models

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Recommended by the experts and mentors at MentorCruise

Introducing MLOps

More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. ...

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Additional MLops Reading

These books are not required for you to learn MLops, but they are highly recommended for you to deepen your knowledge.

Practical MLOps

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. ...

Recommended by the experts and mentors at MentorCruise

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

Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they're data dependent, with data varying wildly from one use case to the next. In this book, you'll learn a holistic approach to designing ML systems that are reliable, scalable, maintainable, and adaptive to changing environme…

Recommended by the experts and mentors at MentorCruise

Machine Learning Engineering with Python: Manage the Production Life Cycle of Machine Learning Models Using MLOps with Practical Examples

Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environmentsKey FeaturesExplore hyperparameter optimization and model ...

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Engineering MLOps: Rapidly Build, Test, and Manage Production-ready Machine Learning Life Cycles at Scale

Get up and running with machine learning life cycle management and implement MLOps in your organizationKey FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning ...

Recommended by the experts and mentors at MentorCruise

Machine Learning Engineering in Action

Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production.In Machine Learning Engineering in Action, ...

Recommended by the experts and mentors at MentorCruise

Reliable Machine Learning

Whether you're part of a small startup or a multinational corporation, this practical book shows data scientists, software and site reliability engineers, product managers, and business owners how to run and establish ML reliably, effectively, and accountably within your organization. ...

Recommended by the experts and mentors at MentorCruise

Specializations and Deeper MLops Knowledge

You've got your basics in order – time to move on to some advanced and specialized concepts. MLops is evolving every day, these books can help you master it.

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

This LLM book provides practical insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps' best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter Notebooks, focusing on how to build production-grade end-to-end LLM systems.
Throughout this book, you will l…

Recommended by the experts and mentors at MentorCruise

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I’m a Lead AI Engineer with over a decade of hands-on experience designing, building and operationalizing intelligent systems. Currently heading AI strategy at PostNL’s Centre of Excellence, I architect end-to-end GenAI, NLP, computer-vision and predictive-analytics solutions on AWS, while driving governance, data quality and ethical compliance under GDPR and the …

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Hi, I’m Georgios. I’m a Principal Engineer with 13 years of experience building and scaling systems at companies like Meta, Amazon, Google, Microsoft, and Samsung. My work has spanned machine learning, system design, and platform engineering—often leading high-impact teams that delivered production-ready solutions at global scale. I’ve conducted 400+ interviews …

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#Career Path * Started as an intern at Tinkoff Bank – best IT bank in Russia, progressed to a Middle+ engineer at Microsoft (currently working at Microsoft Bing, improving the shopping segment search experience through ML/DL) * Worked as an ML Engineer at Microsoft, Yandex (Russian tech giant), X5 Retail …

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  • "Naz is an amazing person and a wonderful mentor. She is supportive and knowledgeable with extensive practical experience. Having been a manager at Netflix, she also knows a ton about working with teams at scale. Highly recommended."

  • "Brandon has been supporting me with a software engineering job hunt and has provided amazing value with his industry knowledge, tips unique to my situation and support as I prepared for my interviews and applications."

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