Top MLOps Books to Read in 2024

Machine Learning Operations (MLOps) refer to the set of practices for enhanced communication and collaboration during a machine learning project lifecycle. It involves principles like dataset validation, collaborative culture, application monitoring, reproducibility, etc., and ensures faster deployment, improved productivity, and reliability. With the rapid advancements in machine learning (ML), there has been an increase in the demand for MLOps specialists as well. This article lists the top MLOps books one should read in 2024 to learn this essential skill.

Machine Learning Design Patterns

“Machine Learning Design Patterns” covers the most common problems in machine learning and its solutions. The book teaches how to build robust training loops and how to deploy scalable ML systems.

Introducing MLOps

This book introduces the fundamentals of MLOps to help data scientists operationalize machine learning models. The book also teaches how to design MLOps life cycle to ensure that the models are unbiased, fair, and explainable.

Designing Machine Learning Systems

This book teaches how to design reliable and scalable machine-learning systems by using actual case studies. The book provides a comprehensive guide on how to automate the process, develop a monitoring system, and develop responsible ML systems.

Machine Learning Engineering

This book covers the different machine learning engineering best practices and design patterns. It explains the ML project lifecycle while focusing on best practices for building and deploying ML solutions.

Machine Learning Engineering with Python 

This is a practical guide to building scalable solutions that solve real-world problems. The book uses Python to explain the concepts and provides various examples to simplify learning. Additionally, the book also covers the latest tools and frameworks, covering Generative AI and LangChain.

Reliable Machine Learning

This book provides a guide on running and establishing ML models reliably, effectively, and accountably. The authors also demonstrate how to apply the SRE mindset to machine learning and the importance of effective production.

Building Machine Learning Pipelines

This book covers how to automate model life cycles with TensorFlow. It also covers orchestrating the pipelines with Apache Beam, Apache Airflow, and Kubeflow Pipelines. Additionally, it sheds light on topics like data validation, model monitoring, and model quantization.

Practical MLOps

This book teaches how to build production-grade machine learning systems and how to maintain them. It provides insights on how to choose the correct MLOps tools for a given ML task. The book also covers implementing the solutions in cloud platforms like AWS, Microsoft Azure, and Google Cloud.

Machine Learning in Production

This book is a comprehensive guide to managing the lifecycle of a machine learning project, from development to deployment. It first starts with the fundamental concepts of MLOps and moves on to cover topics like CI/CD, managing the ML life cycle, deployment on cloud platforms, etc.

Implementing MLOps in the Enterprise

This book helps organizations tackle different challenges that occur while moving ML models to production. The authors have taken a production-first approach and teach how to design continuous operational pipelines.

Engineering MLOps

“Engineering MLOps” covers how to get well-versed with various MLOps techniques to build and manage scalable ML life cycles. The book provides real-world examples in Azure to help its readers deploy models securely in production.

Managing Data Science

“Managing Data Science” is better suited for managers because it helps them understand the different data science concepts and methodologies. The book aims to better equip managers to tackle the varied data science challenges they face on a daily basis.

Machine Learning Engineering in Action

This book consists of various tricks and design patterns for developing scalable and secure ML models. It also guides in choosing the right technologies and tools for the project and automating the troubleshooting and logging practices.

Building Machine Learning Powered Applications: Going from Idea to Product

This book teaches the necessary skills to design, build, and deploy ML-powered applications. Readers also get the opportunity to build an example ML-driven application from scratch throughout the course of the book.

Machine Learning Engineering on AWS

This book covers the numerous AWS services that help in creating scalable and secure ML systems and MLOps pipelines. It covers tools like AWS SageMaker, AWS EKS, AWS Lambda, etc.

Data Science Solutions on Azure

This book is a guide on using Microsoft Azure tools to develop data-driven solutions. It provides a comprehensive understanding of the ML life cycle and how to efficiently productionize workloads. This book is ideal for data scientists deploying ML solutions on Azure.

Continuous Machine Learning with Kubeflow 

This book provides an extensive knowledge of deploying ML pipelines using Docker and Kubernetes. The book explains how to deploy ML applications with TensorFlow training and how to serve with Kubernetes. It also covers how Kubernetes can thoroughly help with specific projects.

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