dcaponi / productionized_iris_model

an api endpoint for leveraging a random forest trained with iris dataset

Productionized Iris Model

What?

I trained a random forest to recognize irises and make predictions of the species based on some parameters

Then, I save the model to a pickle, and load it up in a flask app and expose a POST endpoint

How?

API

run docker build -t iris_api:latest . then docker run --rm -d -p 5000:5000 iris_api:latest then curl -X POST localhost:5000/prediction -d '{"sepal_length": 5.7, "sepal_width": 3.8, "petal_length": 1.7, "petal_width": 0.3}' -H "Content-Type:application/json"

feel free to change up the numbers there.

Notebook

Have jupyter installed, then run jupyter notebook TrainModel.ipynb

Why?

partly for fun, partly to learn something, partly a jumping off point for other ML related things.