E-Books →MLOps with Ray Best Practices and Strategies for Adopting Machine Learning Operations

The book delves into this engineering discipline's aspects and components and explores best practices and case studies. Adopting MLOps requires a sound strategy, which the book's early chapters cover in detail. The book also discusses the infrastructure and best practices of Feature Engineering, Model Training, Model Serving, and Machine Learning Observability. Ray, the open source project that provides a unified framework and libraries to scale machine learning workload and the Python application, is introduced, and you will see how it fits into the MLOps technical stack.
This book is intended for machine learning practitioners, such as machine learning engineers, and data scientists, who wish to help their company by adopting, building maps, and practicing MLOps.
What You'll LearnGain an understanding of the MLOps disciplineKnow the MLOps technical stack and its componentsGet familiar with the MLOps adoption strategyUnderstand feature engineering
Who This Book Is For
Machine learning practitioners, data scientists, and software engineers who are focusing on building machine learning systems and infrastructure to bring ML models to production
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