Containerization and Deployment Preparation
You have constructed a functional FastAPI application capable of serving machine learning model predictions and validated its behavior through testing. The focus now shifts to preparing this application for deployment outside your local development setup. Ensuring consistent operation across different environments is a common challenge, which containerization effectively addresses.
This chapter concentrates on packaging your application using Docker. You will learn to create a Dockerfile tailored for a FastAPI service, incorporating the application code, Python dependencies, and necessary machine learning model artifacts. We will proceed through the steps of building a Docker image and running it as a container. Additionally, you will learn strategies for managing application configuration within containers using environment variables and understand how to set up a production-grade ASGI server like Uvicorn, often managed by Gunicorn, to serve your application efficiently. By the end of this chapter, you will have a containerized version of your ML API ready for deployment pipelines.
Sections
6.1 Introduction to Docker for Application Packaging
6.2 Writing a Dockerfile for a FastAPI Application
6.3 Including ML Models in Docker Images
6.4 Building and Running Docker Containers
6.5 Managing Python Dependencies within Docker
6.6 Configuring Applications with Environment Variables
6.7 Preparing for Production Deployment (Gunicorn/Uvicorn)
6.8 Hands-on Practical: Containerizing the ML API
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