MLOps is a relatively new but rapidly growing practice. The books highlighted in this article provide practical guides to understand the practice as a machine learning project stakeholder and enable your ML workflows' practices.
MLOps is a systematic operationalization of machine learning workflows. It is the practice of applying DevOps and ITOps practice to data science, AI, machine learning workflows to make the process efficient, flexible, reproducible, and manageable.
This article is a handpicked list of some of the best books you should read as a data scientist, machine learning engineer, DevOps engineer, and project manager to learn about the practice and practically apply it to machine learning workflows.
Accelerated DevOps with AI, ML & RPA: Non-Programmer's Guide to AIOPS & MLOPS" by Stephen Fleming
Accelerated DevOps with AI, ML & RPA is a walkthrough story of how artificial intelligence and machine learning is applied to IT operations and how IT operations is applied to artificial intelligence and machine learning development workflow. It explores the impact of AI and machine learning in today's digital space and takes predictive speculation of the further effects the technology will have on IT operations.
Written and published by Stephen Fleming in 2019, the book takes a less technical approach to explain the technology for easy assimilation by readers in various fields.
Introducing MLOps: How to Scale Machine Learning in the Enterprise by Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann
Introducing MLOps does exactly what its title says- introduces MLOps. The book, written by nine authors, explicitly explains what MLOps is, the problems that brought about the practice, and other key concepts that are useful for data science and ML professionals to develop and deploy machine learning models to production.
Based on experience, the authors explain the processes involved in a successful machine learning life cycle and how to implement them in an enterprise-scale workflow.
Engineering MLOps by Emmanuel Raj
Starting with the fundamental concepts of MLOps, Emmanuel Raj's Engineering MLOps acquaints you with machine learning and software development workflow. It then gives a practical, real-life explanation of continuously training, building, deploying, monitoring, and governing machine learning models from the development to production phase using the MLOps practice. The book aims to give you a hands-on experience of implementing the MLops practice in your organization.
Practical MLOps by Noah Gift, Alfredo Deza
As the title depicts, Practical MLOps takes you through an insightful and practical tour on the end-to-end operationalization of machine learning models using MLOps. It gives you a foundational and actionable knowledge of various ML development tools and methods; ML implied operations methods and a combination of the two in MLOps while teaching how to implement all you’ve learned in a real production environment using AWS, Azure, or GCP as the pilot.
Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn
Three authors, Valliappa Lakshmanan, Sara Robinson, Michael Munn, came together to explain in detail some of the most complex and complex design problems throughout the ML workflow. Machine Learning Design Patterns explains in detail 30 different design patterns for representing data in machine learning models to model the models more flexible, reproducible, and operational-which is the end goal of MLOps.
Machine Learning Engineering by Andriy Burkov
The book provides a distinct view of the end-to-end machine learning project lifecycle, focusing on design thinking and best practices for building and deploying a production-ready machine learning model.
Building Machine Learning Pipelines by Hannes Hapke, Catherine Nelson
Building Machine Learning Pipelines hammers the importance of DevOps engineers, data scientists, machine learning engineers, and project managers in building and deploying machine learning projects to production.
Hannes Hapke, Catherine Nelson explain how to use TensorFlow, Apache Beam, Kubeflow, and various other tools to build an efficient end-to-end machine learning pipeline and improve the operationality of machine learning models in the production environment.
Managing Data Science by Kirill Dubovikov
This book, Managing Data Science, shows how individuals and organizations can transform their machine learning project development workflow into a more flexible, manageable, and reproducible process through DevOps and ModelOps.
It provides a practical guide to effectively apply the tips and best practices discussed in the book to achieve an efficiently manageable artificial intelligence and machine learning project lifecycle.
Data Teams by Jesse Anderson
Like DevOps, MLOps is a practice that involves a collaboration of all professionals tangled in the machine learning model development process. This book, Data Teams, explains how to build a manageable team of data scientists and machine learning engineers with operationality, flexibility, manageability, and deployability of machine learning and data science projects into production as their common goal. It discusses, to a length, the pitfalls and challenges faced in data science and how to overcome them as a team.
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