Top MLOps Books In 2021 – Analytics India Magazine


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Machine learning is getting mainstreamed as many organisations have integrated or are trying to integrate ML systems into their products and platforms. MLOps is the branch of ML that unifies ML systems development (dev) and ML systems deployments (ops)
We have curated a list of top MLOps books to help you get a handle on the subject (in no particular order).
By Andriy Burkov
Image Credits: Amazon
The Machine Learning Engineering book is one of the most complete applied AI books out there and is filled with best practices and design patterns of building reliable machine learning solutions at scale. Andriy Burkov has a PhD in AI and is currently the machine learning team leader at Gartner. 
Find it here.
By David Sweenor, Dev Kannabiran, Thomas Hill, Steven Hillion, Dan Rope and Michael O’Connell
Image Credits: O’Reilly
Many analytics and machine learning (ML) models never make it to production. In this book, six experts in data analytics offer a four-step approach— Build, Manage, Deploy and Integrate, and Monitor—for creating ML-infused applications. The book covers:

Find it here.
By Emmanuel Ameisen
Image Credits: O’Reilly
In this book, author Emmanuel Ameisen will help you build an ML-driven application from initial idea to deployed product.
Find it here
By Hannes Hapke, Catherine Nelson
Image Credits: Amazon
In this book, authors Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. The book covers:
Find it here.
by Noah Gift, Alfredo Deza
Image Credits: O’Reilly
This book will take you through what MLOps is (and how it differs from DevOps) and explains how to operationalise your machine learning models. The book is a primer on in MLOps tools and methods (along with AutoML and monitoring and logging), and teaches you to implement them in AWS, Microsoft Azure, and Google Cloud.
Find it here.
By Mark Treveil & Dataiku Team
Image Credits: Amazon
This book, by author Mark Treveil & Dataiku Team, helps understand the key concepts of MLOps to help data scientists and application engineers operationalise ML models to drive real business change and maintain and improve models over time. The book covers:
Find it here.

By Sridhar Alla, Suman Kalyan Adari
Image Credits: O’Reilly
The book covers MLFlow and ways to integrate MLOps into your existing code, to easily track metrics, parameters, graphs, and models. It will guide you through the process of deploying and querying your models with AWS SageMaker, Microsoft Azure, and Google Cloud.
Find it here.
By Mark Treveil, Lynn Heidmann
Image Credits: O’Reilly
In this book, authors Lynn Heidmann and Mark Treveil from Dataiku introduce the data science-ML-AI project lifecycle. The book covers:
Find it here.
by Emmanuel Raj
Image Credits: Amazon
The book provides in-depth knowledge of MLOps using real-world examples to assist you in writing programmes, training robust and scalable ML models, and constructing ML pipelines to train and deploy models safely in production. The book covers:
Find it here.
Kumar Gandharv, PGD in English Journalism (IIMC, Delhi), is setting out on a journey as a tech Journalist at AIM. A keen observer of National and IR-related news. He loves to hit the gym. Contact: [email protected]


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