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

17. Other Books You May Enjoy

Setting up the production infrastructure

In this section, we will set up the required infrastructure to serve our business use case (to predict weather conditions – raining or not raining at the port of Turku to plan and optimize resources at the port). We will set up an autoscaling Kubernetes cluster to deploy our ML model in the form of a web service. Kubernetes is an open source container orchestration system for automating software application deployment, scaling, and management. Many cloud service providers offer a Kubernetes-based infrastructure as a service. Similarly, Microsoft Azure provides a Kubernetes-based infrastructure as a service called Azure Kubernetes Service (AKS). We will use AKS to orchestrate our infrastructure.

There are multiple ways to provision an autoscaling Kubernetes cluster on Azure. We will explore the following two ways to learn about the different perspectives of infrastructure provisioning:

Azure Machine Learning workspace portal...

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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

Setting up the production infrastructure

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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

Setting up the production infrastructure

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