End-to-End-ML-Recipe (employee burnout prediction)

Setup conda env

Setup conda virtual environment from the environment.yml file of this repo.

conda env create -f environment.yml
conda activate emp_burnout

Install the codebase as a package (setup.py)

pip install .

For running the servers (MLFlow server, Minio, NGINX), start docker-compose as:
```
docker-compose up 
```
- Make sure to configure the volumes (check host paths) in the docker-compose.yml file as needed.
- The server is only needed for tracking purposes. If you don't need mlflow tracking, you can opt to ignore this step.
For the client REST apis, start the app with uvicorn using:
```
uvicorn emp_burnout.app:app
```
Head over to localhost:8000/docs to view the swagger ui for the exposed REST apis.
For the job config files, refer to configs/train.yml and configs/predict.yml.
- The train.yml also contains the hyperparameters for training. One can modify them too.