You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Product type Paperback

Published in Apr 2021

Publisher Packt

ISBN-13 9781800562882

Length 370 pages

Edition 1st Edition

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Emmanuel Raj

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Table of Contents (18) Chapters

Preface

1. Section 1: Framework for Building Machine Learning Models

2. Chapter 1: Fundamentals of an MLOps Workflow FREE CHAPTER

3. Chapter 2: Characterizing Your Machine Learning Problem

4. Chapter 3: Code Meets Data

5. Chapter 4: Machine Learning Pipelines

6. Chapter 5: Model Evaluation and Packaging

7. Section 2: Deploying Machine Learning Models at Scale

8. Chapter 6: Key Principles for Deploying Your ML System

9. Chapter 7: Building Robust CI/CD Pipelines

10. Chapter 8: APIs and Microservice Management

11. Chapter 9: Testing and Securing Your ML Solution

12. Chapter 10: Essentials of Production Release

13. Section 3: Monitoring Machine Learning Models in Production

14. Chapter 11: Key Principles for Monitoring Your ML System

15. Chapter 12: Model Serving and Monitoring

16. Chapter 13: Governing the ML System for Continual Learning

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Hands-on deployment (for the business problem)

In this section, we will learn how to deploy solutions for the business problem we have been working on. So far, we have done data processing, ML model training, serialized models, and registered them to the Azure ML workspace. In this section, we will explore how inference is performed on the serialized model on a container and an auto-scaling cluster. These deployments will give you a broad understanding and will prepare you well for your future assignments.

We will use Python as the primary programming language, and Docker and Kubernetes for building and deploying containers. We will start with deploying a REST API service on an Azure container instance using Azure ML. Next, we will deploy a REST API service on an auto-scaling cluster using Kubernetes (for container orchestration) using Azure ML, and lastly, we will deploy on an Azure container instance using MLflow and an open source ML framework; this way, we will learn how to...

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Hands-on deployment (for the business problem)

Authors (1)

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You're reading from Engineering MLOps Rapidly build, test, and manage production-ready machine learning life cycles at scale

Table of Contents (18) Chapters

Hands-on deployment (for the business problem)

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