Engineering MLOps: Rapidly build test and manage production-ready machine learning life cycles at scale


Engineering MLOps: Rapidly build test and manage production-ready machine learning life cycles at scale
Authors: Emmanuel Raj
Year: 2021
Publisher: Packt Publishing
Language: English
ISBN 13: 9781800562882
ISBN 10: 1800562888
Categories: Computers, Computer Science
Pages: 320 / 319

Availability: 5000 in stock

SKU: 9781800562882 Categories: ,

Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale Emmanuel Raj
Get up and running with machine learning life cycle management and implement MLOps in your organizationKey FeaturesBecome well-versed with MLOps techniques to monitor the quality of machine learning models in productionExplore a monitoring framework for ML models in production and learn about end-to-end traceability for deployed modelsPerform CI/CD to automate new implementations in ML pipelinesBook DescriptionMLOps 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 familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then 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 learnFormulate data governance strategies and pipelines for ML training and deploymentGet to grips with implementing ML pipelines, CI/CD pipelines, and ML monitoring pipelinesDesign a robust and scalable microservice and API for test and production environmentsCurate your custom CD processes for related use cases and organizationsMonitor ML models, including monitoring data drift, model drift, and application performanceBuild and maintain automated ML systemsWho this book is forThis 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.Table of ContentsFundamentals of MLOps WorkflowCharacterizing your Machine learning problemCode Meets DataMachine Learning PipelinesModel evaluation and packagingKey principles for deploying your ML systemBuilding robust CI and CD pipelines APIs and microservice ManagementTesting and Securing Your ML SolutionEssentials of Production ReleaseKey principles for monitoring your ML systemModel Serving and MonitoringGoverning the ML system for Continual Learning Categories:
Computers – Computer Science
Packt Publishing Language:
ISBN 10:
ISBN 13:
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Engineering MLOps: Rapidly build test and manage production-ready machine learning life cycles at scale

Availability: 5000 in stock