Flask ML Model CD Pipeline Tutorial

The data set in this exercise is from the blog on Flask Tutorial at Build the first Flask Python web app framework. This codebase is based on the GCP Pipeline tutorial at ML Deployment on Cloud

ML Model Flask-Deployment

This project demonstrates how a Flask ML app can be deployed on Google Cloud Platform using Docker container and YAML files that are useful to build continuous deployment (CD) pipelines.

Prerequisites (requirements.txt)

Scikit Learn
Pandas
Numpy
Flask

Project Structure

All the application files are contained in the folder 'app_files'. The goal is to build an ML model using Decision Tree Classifier

model.py - This contains code fot our Machine Learning model (Decision Tree model) to predict employee salaries absed on trainign data in '50_Startup.csv' file.
app.py - This contains Flask APIs that receives employee details through GUI or API calls, computes the precited value based on our model and returns it.
request.py - This uses requests module to call APIs already defined in app.py and dispalys the returned value.
templates - This folder contains the HTML template to allow user to enter employee detail and displays the predicted employee salary.

Running the project

Ensure that you are in the project home directory. Create the machine learning model by running below command -

python model.py

This would create a serialized version of our model into a file model.pkl

Run app.py using below command to start Flask API

python app.py

The flask app will run on http://0.0.0.0:8080/ (localhost)

Deploying on Google Cloud

Create a Google Cloud Run trigger at: https://console.cloud.google.com/cloud-build/triggers
Check the deployed app at: https://console.cloud.google.com/run

You now have a deployed app

Clean up

Delete the triggers on the page https://console.cloud.google.com/cloud-build/triggers
Delete the app at: https://console.cloud.google.com/run