A simple framework for machine learning models post deployment, which in this project, is applied to an attrition risk assessment system.

This repository follows the following structure:

.
├── sourcedata/                       # Directory storing data for model development
├── practicedata/                     # Directory storing data for practice
├── testdata/                         # Directory storing data for testing
├── ingesteddata/                     # Directory storing clean data
├── practicemodels/                   # Directory storing trial models
├── models/                           # Directory storing models build with sourcedata
├── productiondeployment/             # Directory soring deployed model
├── images/                           # Directory soring images  
├── ingestion.py   
├── training.py                     
├── scoring.py         
├── deployment.py
├── reporting.py    
├── diagnostics.py   
├── app.py    
├── apicalls.py  
├── fullprocess.py
├── wsgi.py                             
├── config.json
├── slice_output.txt
├── cronjob.txt
└── README.md

To install the requirements, run the following command:

pip install -r requirements.txt

To run the app locally, run the following command:

python app.py

To run the API calls, open a new CL/terminal tab, then run the following command:

python apicalls.py

To run other scripts individually, run the following command:

python {SCRIPT_NAME}.py

The entire monitoring process, which is orchestrated by the fullprocess.py script, follows the diagram below:

This process can be setup to run automatically at specific intervals using cron. The shell command used to do this can be found in cronjob.txt. This command tells the fullprocess.py script to run every 10 minutes.

For more information on cron expressions, check out their documentation.

First, the script will check for new data under the input_folder_path, defined in config.json. If new data is present, the ingestion process, defined by the ingestion.py script, will run and produce a new dataset, stored under output_folder_path, again defined in config.json.

In this example, the paths are just subdirectories. However, the same concept can be extended to other data storage options.

Using the new data produced in the step above, the script will proceed to test the existing model, stored in prod_deployment_path, against this new data. The model scores are recorded, and if model drift is detected, the script will initiate the retraining and redeployment steps (defined in the training.py and deployment.py scripts), before finally initiating the reporting and diagnostics steps (defined in the reporting.py and diagnostics.py scripts).

Other than model drift, data drift will also be assessed and reported using EvidentlyAI.

For more information on EvidentlyAI, check out their documentation.

The codes for the Flask API are defined in the app.py script, which can be run via the command line or terminal. As models are retrained and redeployed, the app will continuously update as well. To test the endpoints defined in the app.py script, run apicalls.py in a separate CL/terminal tab.

For more information on Flask, check out their documentation.