Название: A Guide to Implementing MLOps: From Data to Operations
Автор: Prafful Mishra
Издательство: Springer
Год: 2025
Страниц: 144
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

Over the past decade, Machine Learning has come a long way, with organisations of all sizes exploring its potential to extract valuable insights from data. However, despite the promise of Machine Learning, many organisations need help deploying and managing Machine Learning models in production. This is where MLOps comes in. MLOps, or Machine Learning Operations, is an emerging field that focuses on the deployment, management, and monitoring of Machine Learning models in production environments.

MLOps combines the principles of DevOps with the unique requirements of Machine Learning, enabling organisations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and emerging trends in the field. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organisations.

MLOps, or Machine Learning operations, is an emerging field that focuses on the deployment, management, and monitoring of Machine Learning models in production environments. This combines the principles of DevOps with the unique requirements of Machine Learning, enabling organizations to build and deploy models at scale while maintaining high levels of reliability and accuracy. This book is a comprehensive guide to MLOps, providing readers with a deep understanding of the principles, best practices, and details on how to implement it at scale. From training models to deploying them in production, the book covers all aspects of the MLOps process, providing readers with the knowledge and tools they need to implement MLOps in their organizations.

As most of the ML work is done in Python today (thanks to NumPy and Scikit-learn) even though rust is catching up pretty fast, the packaging of the code is very important, as it can enable one to unlimited possibilities. And we are gonna talk about Python projects specifically here. Even though it’s such a popular language, there are clear drawbacks when it comes to strong typing or sub-dependency management. Afterall, the language was made to provide easy experimentation and not productionising scaled systems. To make this better, there are a number of tools like Poetry, Pipenv, Conda that have made their way into the usual life of a Python programmer. Having said this, it is very important for an ML project to handle dependencies, have proper lineage tracking and reproducibility in the Python code. One of the best ways to achieve this is to use a single python package per code repository you have. This would make your life easy on a rainy day, as well as make the versioning of ML assets very handy at scale.

The book is aimed at data scientists, Machine Learning engineers, and IT professionals who are interested in deploying Machine Learning models at scale. It assumes a basic understanding of Machine Learning concepts and programming, but no prior knowledge of MLOps is required. Whether you're just getting started with MLOps or looking to enhance your existing knowledge, this book is an essential resource for anyone interested in scaling Machine Learning in production.

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