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.

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. ...

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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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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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MLOps Engineering at Scale

Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools!In MLOps Engineering at Scale you will learn: ...

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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. ...

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Building Machine Learning Pipelines

Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. ...

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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.

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 ...

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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. ...

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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. ...

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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…

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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

Designing Machine Learning Systems

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. ...

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

This list is curated by MentorCruise and can include Amazon affiliate links. Have any other suggestions? Add here.

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Lead Machine Learning Engineer @ Institut Polytechnique de Paris with the Hi! PARIS Research center https://www.hi-paris.fr/ Ex-Amazon Data Science & DHL Consulting I lead a team of 8 engineers (ml ops engineers, data scientists, data engineers) creating state-of-the-art open-source python packages. Before becoming a manager, my role involved being an …

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Experience in Quant Finance, Engineering, Data Science, Startups. My background in applied mathematics and my experience in coding helped me with this challenging transition. Throughout my ten-year career, I have been involved in R&D as well as product development within both the financial and engineering industries. I can help you …

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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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