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

Developing a microservice using Docker

In this section, we will package the FastAPI service in a standardized way using Docker. This way, we can deploy the Docker image or container on the deployment target of your choice within around 5 minutes.

Docker has several advantages, such as replicability, security, development simplicity, and so on. We can use the official Docker image of fastAPI (tiangolo/uvicorn-gunicorn-fastapi) from Docker Hub. Here is a snippet of the Dockerfile:

FROM tiangolo/uvicorn-gunicorn-fastapi:python3.7
COPY ./app /app
RUN pip install -r requirements.txt
EXPOSE 80
CMD ["uvicorn", "weather_api:app", "--host", "0.0.0.0", "--port", "80"]

Firstly, we use an official fastAPI Docker image from Docker Hub by using the FROM command and pointing to the image – tiangolo/uvicorn-gunicorn-fastapi:python3.7. The image uses Python 3.7, which is compatible with fastAPI. Next, we copy the app folder...

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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

Developing a microservice using Docker

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

Developing a microservice using Docker

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