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).
1| Machine Learning Engineering
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.
2| ML Ops: Operationalizing Data Science
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:
- Fulfil data science value by reducing friction throughout ML pipelines and workflows.
- Constantly refine ML models through retraining, periodic tuning, and even complete remodelling to ensure long-term accuracy.
- Design the MLOps lifecycle to ensure that people-facing models are unbiased, fair, and explainable.
Find it here.
3| Building Machine Learning Powered Applications
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.
- Part I dives into how to plan an ML application and then measure success.
- Part II focuses on how to build a working ML model.
- Part III demonstrates ways to improve the model to meet your original vision.
- Part IV covers deployment and monitoring strategies.
Find it here
4| Building Machine Learning Pipelines
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:
- Steps to build a machine learning pipeline.
- Building pipeline using components from TensorFlow Extended.
- Orchestrating machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines.
- Working with data using TensorFlow Data Validation and TensorFlow Transform.
Find it here.
5| Practical MLOps
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.
6| Introducing MLOps
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:
- Refining ML models through retraining, periodic tuning, and complete remodelling to ensure long-term accuracy.
- Designing the MLOps life cycle to minimise organisational risks with models that are unbiased, fair, and explainable.
- Operationalising ML models for pipeline deployment and for external business systems that are more complex and less standardised.
Find it here.
7| Beginning MLOps with MLFlow: Deploy Models in AWS SageMaker, Google Cloud, and Microsoft Azure
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.
8| What Is MLOps?
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:
- Detailed components of ML model building, including how business insights can provide value to the technical team.
- Monitoring and iteration steps in the AI project lifecycle.
- How components of a modern AI governance strategy are intertwined with MLOps.
Find it here.
9| Engineering MLOps
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:
- Designing a robust and scalable microservice and API for test and production environments.
- Monitoring ML models, including monitoring data drift, model drift, and application performance.
- Building and maintaining automated ML systems.
Find it here.
