ML-Model-Scoring-and-Monitoring: A Python repository from PraveenKumar-Rajendran

Execution Guide

Configuration

Update config.json with your desired settings. This file will be used across all scripts and processes.

Running Individual Scripts

Execute the following commands to run the individual scripts:

python ingestion.py
python training.py
python scoring.py
python deployment.py 
python diagnostics.py 
python reporting.py

Testing Endpoints Manually

Start the Flask application in a separate terminal session:
```
python app.py
```

Use the following curl commands in another terminal session to test the endpoints:

curl 127.0.0.1:8000/
curl 127.0.0.1:8000/prediction
curl 127.0.0.1:8000/prediction?filename=testdata/testdata.csv
curl 127.0.0.1:8000/scoring
curl 127.0.0.1:8000/summarystats
curl 127.0.0.1:8000/diagnostics

Automated API Calls

To make automated API calls and store the combined outputs, run:

python apicalls.py

Automated Model Scoring and Monitoring

To initiate the automated model scoring and monitoring process, run:

python fullprocess.py

Setting Up Automation with Crontab

To automate the process with crontab:

Open the crontab editor:
```
crontab -e
```
Add the following line to schedule the script (ensure you replace with your Python path and script path):
```
*/10 * * * * /usr/bin/env python3 /path/to/fullprocess.py
```

Make sure to replace the Python path and fullprocess.py script path with the correct paths for your environment.