MLOps with Ray
Best Practices and Strategies for Adopting Machine Learning Operations
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Beschreibung
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.
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 Learn
- Gain an understanding of the MLOps discipline
- Know the MLOps technical stack and its components
- Get familiar with the MLOps adoption strategy
- Understand feature engineering
Understand how to use MLOps as an engineering discipline to help with the challenges of bringing machine learning models to production quickly and consistently. This book will help companies worldwide to adopt and incorporate machine learning into their processes and products to improve their competitiveness.
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 Learn
- Gain an understanding of the MLOps discipline
- Know the MLOps technical stack and its components
- Get familiar with the MLOps adoption strategy
- Understand 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
Covers up-to-date best practices and innovations in MLOps Explains MLOps with case studies where it has been successfully adopted in organizations Explains Ray open source project and how it might fit into the MLOps stack
Autor*in
Hien Luu
Themen in »MLOps with Ray«
Ray AIR ML infrastructure Machine Learning orchestration MLOps Feature Engineering Ray Machine Learning Observability