Emmanuel Raj - Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
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- Book:Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale
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Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale: summary, description and annotation
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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
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 familiarizing you with the MLOps workflow so you can start writing programs to train ML models. Then youll 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. Youll 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, youll apply the knowledge youve gained to build real-world projects.
By the end of this ML book, youll 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
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.
Table of Contents- 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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Rapidly build, test, and manage production-ready machine learning life cycles at scale
Emmanuel Raj
BIRMINGHAMMUMBAI
Copyright 2021 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Group Product Manager: Kunal Parikh
Publishing Product Manager: Aditi Gour
Senior Editor: Mohammed Yusuf Imaratwale
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First published: April 2021
Production reference: 1160421
Published by Packt Publishing Ltd.
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ISBN 978-1-80056-288-2
www.packt.com
Emmanuel Raj is a Finland-based Senior Machine Learning Engineer with 6+ years of industry experience. He is also a Machine Learning Engineer at TietoEvry and a Member of the European AI Alliance at the European Commission. He is passionate about democratizing AI and bringing research and academia to industry. He holds a Master of Engineering degree in Big Data Analytics from Arcada University of Applied Sciences. He has a keen interest in R&D in technologies such as Edge AI, Blockchain, NLP, MLOps, and Robotics. He believes the best way to learn is to teach, he is passionate about sharing and learning new technologies with others.
Magnus Westerlund (DSc) is a principal lecturer in information technology and director of the master's degree programme in big data analytics at Arcada University of Applied Sciences in Helsinki, Finland. He has a background in telecoms and information management and earned his doctoral degree in information systems at bo Akademi University, Finland. Magnus has published research in the fields of analytics, IT security, cyber regulation, and distributed ledger technology. His current research topics are smart contract-based distributed security for IoT edge applications and the assessment of intelligent systems. He participates as a technical expert in the Z-inspection network, which works for a Mindful Use of AI (#MUAI).
Stephen Oladele is the co-founder of AgServer, a peer-to-peer knowledge-sharing platform for smallholder farmers in Africa. He also assists in building data science talents at TheGradientBoost, a staffing and recruiting company. He has consulted as a data scientist for companies and individuals, helping them go from business ideas to execution with notable projects in computer vision, business analytics, and NLP (document analysis), using cloud machine learning services such as those in Google Cloud Platform and Microsoft Azure. In his spare time, he loves volunteering. He runs nonprofit organizations helping underrepresented groups in Africa get into AI and technology. He has volunteered with Google and AWS for projects in the past.
Emerson Bertolo is a data scientist and software developer who has created mission-critical software and dealt with big data applications for more than 12 years. In 2016, Bertolo deep-dived into machine learning and deep learning projects by creating AI models using TensorFlow, PyTorch, MXNet, Keras, and Python libraries to bring those models into reality for tech companies from LawTech to security and defense. By merging Agile concepts into data science, Bertolo has been seeking the best blend between Agile software engineering and machine learning research to build time-to-market AI applications. His approach has been build to learn, validate results, research and identify uncertainties, rebuild, and learn again!
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