README.md

Challenge 4

"Finally, Francesco wants to embed your model in a web application, to allow for easy use by his employees. Develop a REST API to expose the model predictions."

We have developed an API and deployed it in a dockerized ECS instance of AWS cloud. It can be found in http://18.234.175.187/. The API works with a GET request with URL parameters following the structure http://18.234.175.187/pricing/{carat}/{color}/{clarity} which responds with a JSON with the features and the price computed from the model developed in the previous challenges. A simple documentation can be found in the root http://18.234.175.187/ while a more technical one in http://18.234.175.187/docs.

The API uses the object of the scikit-learn class Pipeline() that was constructed, trained and saved in the file my_pipeline.joblib in the previous challenge.

The contents of this directory:

• Folder src has the Dockerfile and the API code that can be found together with the PIP requirements.txt libraries to be installed on top of a python:3.9-slim-bullseye system. This is all the code deployed in the AWS ECS instance.
• The src_local folder on the other hand, contains the code used to develop the API in a local Docker container before cloud deployment. In it, another README.md file can be found explaining its use.
• The file API_testing.py is a script to make some tests of the API responses and the requirements.txt found in this folder contains the libraries needed for this testing to work.