ISBN:
9781800562882
Language:
English
Pages:
1 online resource (370 pages)
Edition:
1st edition
Keywords:
Electronic books ; local
;
Electronic books
Abstract:
Get up and running with machine learning life cycle management and implement MLOps in your organization Key Features Become well-versed with MLOps techniques to monitor the quality of machine learning models in production Explore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed models Perform CI/CD to automate new implementations in ML pipelines Book Description MLOps is a systematic approach to building, deploying, and monitoring machine learning (ML) solutions. It is an engineering discipline that can be applied to various industries and use cases. This book presents comprehensive insights into MLOps coupled with real-world examples to help you to write programs, train robust and scalable ML models, and build ML pipelines to train and deploy models securely in production. The book begins by showing you how to monitor ML and system performance in production. You'll then move on to explore options for serializing and packaging ML models post-training to deploy them to facilitate machine learning inference, model interoperability, and end-to-end model traceability. You'll understand how to build ML pipelines, continuous integration and continuous delivery (CI/CD) pipelines, and monitoring pipelines to systematically build, deploy, monitor, and govern ML solutions for businesses and industries. Finally, you'll apply the knowledge you've gained to build real-world projects. By the end of this ML book, you'll have a 360-degree view of MLOps and be ready to implement MLOps in your organization. What you will learn Formulate data governance strategies and pipelines for ML training and deployment Get to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelines Design a robust and scalable microservice and API for test and production environments Curate your custom CD processes for related use cases and organizations Monitor ML models, including monitoring data drift, model drift, and application performance Build and maintain automated ML systems Who this book is 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.
Note:
Online resource; Title from title page (viewed April 19, 2021)
,
Mode of access: World Wide Web.
