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

View More author details

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

Testing the API

To test the API for readiness, we will perform the following steps:

Let's start by building the Docker image. For this, a prerequisite is to have Docker installed. Go to your terminal or Command Prompt and clone the repository to your desired location and access the folder 08_API_Microservices. Execute the following Docker command to build the Docker image:
```
docker build -t fastapi .
```
Execution of the build command will start building the Docker image following the steps listed in the Dockerfile. The image is tagged with the name fastapi. After successful execution of the build command, you can validate whether the image is built and tagged successfully or not using the docker images command. It will output the information as follows, after successfully building the image:
```
(base) user ~ docker images   
REPOSITORY   TAG       IMAGE ID       CREATED     ...
```

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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

Testing the API

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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

Testing the API

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