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Engineering MLOps

You're reading from Engineering MLOps

Product type Book

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Pages 370 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Emmanuel Raj

Table of Contents (18) Chapters

Preface

Section 1: Framework for Building Machine Learning Models

Chapter 1: Fundamentals of an MLOps Workflow

Chapter 2: Characterizing Your Machine Learning Problem

Chapter 3: Code Meets Data

Chapter 4: Machine Learning Pipelines

Chapter 5: Model Evaluation and Packaging

Section 2: Deploying Machine Learning Models at Scale

Chapter 6: Key Principles for Deploying Your ML System

Chapter 7: Building Robust CI/CD Pipelines

Chapter 8: APIs and Microservice Management

Chapter 9: Testing and Securing Your ML Solution

Chapter 10: Essentials of Production Release

Section 3: Monitoring Machine Learning Models in Production

Chapter 11: Key Principles for Monitoring Your ML System

Chapter 12: Model Serving and Monitoring

Chapter 13: Governing the ML System for Continual Learning

Other Books You May Enjoy

Old is gold – REST API-based microservices

Old is gold. Plus, it's better to start somewhere where there are various API protocols. The Representational State Transfer (REST) protocol has become a gold standard for many applications over the years, and it's not so very different for ML applications today. The majority of companies prefer developing their ML applications based on the REST API protocol.

A REST API or RESTful API is based on REST, an architectural method used to communicate mainly in web services development.

RESTful APIs are widely used; companies such as Amazon, Google, LinkedIn, and Twitter use them. Serving our ML models via RESTful APIs has many benefits, such as the following:

Serve predictions on the fly to multiple users.
Add more instances to scale up the application behind a load balancer.
Possibly combine multiple models using different API endpoints.
Separate our model operating environment from the user...

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Authors (1)

Emmanuel Raj

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.

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