This repository explains how to train, monitor, make versions, register and server Machine Learning Models using MLFlow.
It also shows how to deploy Machine Learning Models as a Microservice using FastAPI and MLFLOW.
####### Create Python Virtual Environment and install all dependencies as follows ######
create venv using command: python -m venv venvname
activate venv: venvname\Scripts\Activate
install dependencies: pip install -r requirements.txt
run using: uvicorn main:app --reload
Open up the terminal, change to bash terminal and run the following command to run the bash script but first run your mlflow and fastapi server:
curl_iris.sh