📮 A Guide to Implementing MLOps: From Data by Prafful Mishra (.ePUB)+

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. 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.

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