From Model to Service - Building and Dockerizing APIs
Take your trained machine learning model and build a production-ready REST API using FastAPI. Then, learn to package your application and all its dependencies into a portable Docker container.
You've successfully built a data processing pipeline, trained a model, and rigorously evaluated its performance. These are critical milestones. However, a machine learning model only delivers true value when it's deployed - made accessible to end-users or other systems to make predictions on new, unseen data. Moving a model from a development environment (like a Jupyter notebook or local scripts) into a robust, scalable, and reliable production setting presents a distinct set of engineering challenges.
This tutorial bridges that gap. We will take the Bank Marketing prediction model, along with its associated preprocessing pipeline, and deploy it as a fully functional REST API. You'll learn how to package your application using Docker for consistency and portability. Finally, we'll walk through deploying this containerized API to Amazon Web Services (AWS) using Amazon Elastic Container Service (ECS), making your model accessible over the internet.
The focus is on establishing a practical, end-to-end deployment workflow, covering essential steps from API creation to cloud deployment and basic testing. This is where your AI/ML engineering skills culminate in delivering real-world impact.
Tutorial Goals
- Build a REST API for your ML model using FastAPI.
- Use Pydantic for data validation and clear API contracts.
- Load model artifacts efficiently on application startup.
- Test the API locally using interactive documentation.
- Containerize the API application and its dependencies using Docker.
- Build and run the self-contained Docker image locally.