Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale on Azure, 2nd Edition

by Emmanuel Raj

Gain a 360° view of MLOps, CI/CD pipelines, building ML solutions, microservice development, and deploying and monitoring models in production in diverse environments using popular tools. Getting machine learning (ML) models into production continues to remain challenging using traditional software development methods. This book highlights the changing trends of software development over time and solves the problem of integrating ML with traditional software using MLOps. In this new edition of Engineering MLOps, Emmanuel Raj demystifies MLOps to equip you with the skills needed to build your own MLOps pipelines using state-of-the-art tools (MLFlow, DVC, KubeFlow, Locust.io, Docker, Kubernetes, Apache Spark, to name a few) and platforms. You will start by learning the essentials of ML engineering and build ML pipelines to train and deploy models. The book then covers how to implement an MLOps solution for a real-life business problem using Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform (GCP), as well as cloud agnostic tools. You'll also understand how to build continuous integration/deployment (CI/CD) and continuous delivery pipelines to build, test, deploy, and monitor your models. By the end of the book, you will become proficient at building, deploying, and monitoring any ML model with the MLOps process using any tool or platform. This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and IT managers/strategists (such as CTOs and Product Managers). Business leaders in tech companies are also bound to find this book useful. Basic knowledge of machine learning as well as Python programming language is expected.

482 pages, Paperback

Expected publication April 9, 2027