Engineering MLOps
Get to grips with ML lifecycle management and MLOps implementation for your organization. This book will give you comprehensive insights into MLOps coupled with real-world examples in Azure that will teach you how to write programs, train robust and scalable ML models, and build ML pipelines to train, deploy, and monitor models securely in production.
Offered by
Difficulty Level
Intermediate
Completion Time
12h20m
Language
English
About Book
Who Is This Book For?
This MLOps book is for data scientists, software engineers, DevOps engineers, machine learning engineers, and business and technology leaders who want to build, deploy, and maintain ML systems in production using MLOps principles and techniques. Basic knowledge of machine learning is necessary to get started with this book.
Engineering MLOps
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h20m total length
Fundamentals of MLOps Workflow
Characterizing your Machine learning problem
Code Meets Data
Machine Learning Pipelines
Model evaluation and packaging
Key principles for deploying your ML system
Building robust CI and CD pipelines
APIs and microservice Management
Testing and Securing Your ML Solution
Essentials of Production Release
Key principles for monitoring your ML system
Model Serving and Monitoring
Governing the ML system for Continual Learning
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