ML model deployment example

Complete code (including a trained model) to deploy and inference a machine learning model (built on the iris dataset) using Docker and FastAPI.

1. With terminal navigate to the root of this repository

2. Build docker image

docker build -t image_name .

3. Run container

docker run --name container_name -p 8000:8000 image_name

4. Output will contain

INFO: Uvicorn running on http://0.0.0.0:8000

Use this url in chrome to see the model frontend; use http://0.0.0.0:8000/docs for testing the model in the web interface.

5. Query model

1. Via web interface (chrome):

   http://0.0.0.0:8000/docs -> test model

2. Via python client:

   client.py

3. Via curl request:

   curl -X POST "http://0.0.0.0:8000/predict" -H "accept: application/json" -H "Content-Type: application/json" -d '{"features": [5.1, 3.5, 1.4, 0.2]}'

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