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

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.

Key Features

Design a robust and scalable microservice and API for test and production environments

Become well versed in MLOps on Azure and open-source tools, including MLFlow, KubeFlow, Docker, Kubernetes, Apache Airflow/Flink/Spark, GitHub

Implement ML, CI/CD, continuous training, ML monitoring pipelines within your organization

Learn automated Machine Learning systems and ML engineering

Book DescriptionGetting 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 -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.What you will learn

Deploy ML models from the lab environment to production and customize solutions to fit your infrastructure and on-premises needs

Run ML models on Azure and on devices, including mobile phones and specialized hardware

Design a streaming service for inference in real-time with Apache Flink

Explore deployment techniques: A/B testing, phased rollouts, and shadow deployments

Formulate data governance strategies and pipelines for ML training and deployment

Who this book is forThis 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.