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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

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Structuring your MLOps

The primary goal of MLOps is to make an organization or set of individuals collaborate efficiently to build data and ML-driven assets to solve their business problems. As a result, overall performance and transparency are increased. Working in silos or developing functionalities repeatedly can be extremely costly and time-consuming.

In this section, we will explore how MLOps can be structured within organizations. Getting the MLOps process right is of prime importance. By selecting the right process and tools for your MLOps, you and your team are all set to implement a robust, scalable, frugal, and sustainable MLOps process. For example, I recently helped one of my clients in the healthcare industry to build and optimize their MLOps, which resulted in 76% cost optimization (for storage and compute resources) compared to their previous traditional operations.

The client's team of data scientists witnessed having 30% of their time freed up from mundane...

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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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