3 books on MLOps [PDF]
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Books on MLOps (Machine Learning Operations) describe essential principles and best practices required to efficiently develop, deploy and manage AI models.
1. MLOps IN PRACTICE: Automation and Scalability for AI Models
2025 by Diego Rodrigues
This is a good book that clearly explains the MLOps sphere for data scientists and AI engineers. After all, it is no longer enough to develop an effective AI model for some business task. You also need to ensure the implementation, operation of this model in production, its continuous updating. This is what MLOps does. In particular, it is necessary to organize the version control for models and datasets (using DVC, MLflow), automate machine learning pipelines (data loading and cleaning, training, experiments, testing - using Kuberflow or Apache Airflow), use Docker containers to create platform-independent ML models, orchestrate and scale these containers using Kubernetes, set up CI/CD (continuous integration and continuous delivery) of ML models, monitor models in production, create an API for applications, ensure security and compliance.
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2. Implementing MLOps in the Enterprise
2023 by Yaron Haviv, Noah Gift
The authors of this book - Yaron Haviv and Noah Gift - are MLOps veterans. They say that companies typically start the data analysis process by building a model in a lab setting. A small team works on it, somehow manually extracting a data set. As soon as the model is ready and starts showing accurate predictions, the whole IT department begins painful attempts to implement it in production and get real business value. They need to somehow extract and prepare data again, scale, monitor and repair models in production. This leads to huge resource and time loss. So the authors propose an alternative approach - to start not with creating a model, but with designing a continuous operational AI-pipeline that automates most components and is focused on extracting business value from the date.
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3. Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
2021 by Emmanuel Raj
According to Emmanuel Raj (the author of this book) MLOps is a systematic approach to building, deploying and monitoring machine learning-based data analytics systems. This book is a comprehensive guide for applying this approach in practice and with real-world examples. The book will help you learn how to build ML pipelines to train and deploy models in a secure environment. The book starts with a bird's eye view of the overall MLOps process. Then, you will learn step by step: developing machine learning models -> serializing and packaging them -> monitoring the model in production. Finally, you will apply what you have learned to build a real project. This book is intended for data scientists, software engineers, DevOps engineers, machine learning engineers, as well as business and technology leaders. Basic knowledge of machine learning is necessary to understand it.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
1. MLOps IN PRACTICE: Automation and Scalability for AI Models
2025 by Diego Rodrigues
This is a good book that clearly explains the MLOps sphere for data scientists and AI engineers. After all, it is no longer enough to develop an effective AI model for some business task. You also need to ensure the implementation, operation of this model in production, its continuous updating. This is what MLOps does. In particular, it is necessary to organize the version control for models and datasets (using DVC, MLflow), automate machine learning pipelines (data loading and cleaning, training, experiments, testing - using Kuberflow or Apache Airflow), use Docker containers to create platform-independent ML models, orchestrate and scale these containers using Kubernetes, set up CI/CD (continuous integration and continuous delivery) of ML models, monitor models in production, create an API for applications, ensure security and compliance.
Download PDF
2. Implementing MLOps in the Enterprise
2023 by Yaron Haviv, Noah Gift
The authors of this book - Yaron Haviv and Noah Gift - are MLOps veterans. They say that companies typically start the data analysis process by building a model in a lab setting. A small team works on it, somehow manually extracting a data set. As soon as the model is ready and starts showing accurate predictions, the whole IT department begins painful attempts to implement it in production and get real business value. They need to somehow extract and prepare data again, scale, monitor and repair models in production. This leads to huge resource and time loss. So the authors propose an alternative approach - to start not with creating a model, but with designing a continuous operational AI-pipeline that automates most components and is focused on extracting business value from the date.
Download PDF
3. Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
2021 by Emmanuel Raj
According to Emmanuel Raj (the author of this book) MLOps is a systematic approach to building, deploying and monitoring machine learning-based data analytics systems. This book is a comprehensive guide for applying this approach in practice and with real-world examples. The book will help you learn how to build ML pipelines to train and deploy models in a secure environment. The book starts with a bird's eye view of the overall MLOps process. Then, you will learn step by step: developing machine learning models -> serializing and packaging them -> monitoring the model in production. Finally, you will apply what you have learned to build a real project. This book is intended for data scientists, software engineers, DevOps engineers, machine learning engineers, as well as business and technology leaders. Basic knowledge of machine learning is necessary to understand it.
Download PDF
How to download PDF:
1. Install Gooreader
2. Enter Book ID to the search box and press Enter
3. Click "Download Book" icon and select PDF*
* - note that for yellow books only preview pages are downloaded
