Deploying Statistical Models using Flask and Python

This is a project to elaborate how to deploy a statistical model from the VARMAX family using a combination of Flask API deployed using a production ready docker (docker-compose) file.

Project Structure

This project has two major parts :

1. src/models - This folder contains code fot the statistical model, data loading, model optimization, testing, serialization
2. web-app - This folder contains the Flask APIs, web project, serialized model and Docker/Docker-Compose setup files

In addition to the main folders there are some helper folders:

1. notebooks - This folder contains jypiter notebooks with various model experiments
2. diagrams - This folder contains documentation diagrams
3. data - This folder is dedicated to data storage that might be needed for models training and testing

Running the project

1. Ensure that you are in the project home directory. Create the model by running below command from command prompt -

python varmax_model.py

This would create a serialized version of our model into a file model.pkl located in web-app/savedmodels

2. Run app.py using below command to start Flask API

python run.py

or using docker-compose within web-app run

 - docker-compose up -d
 - 

3. Navigate to URL http://127.0.0.1:80 (or) http://localhost:80 You should be able to view the homepage.

4. To access MinIO web console navigate to http://localhost:9101/ (or) http://127.0.0.1:9101/