ML Ops with GitHub Actions, DagsHub and AWS EC2 Instance

This repository can be used for easily setting up a data science or machine learning project with automated training and deployment using GitHub Actions, DagsHub and AWS EC2.

Main Concepts Covered

The following concepts are automatically performed using Github Actions and DagsHub.

• Automatically pull and process data from Github using Pandas and Python.
• Train your model and track experiment with MLFlow and SKlearn.
• Validate the model and save the serialized model and metadata to DVC on DagsHub.
• Deploy your model to AWS EC2 instance.
• Monitor the metrics with MLFlow.
• Retrain if necessary.

Getting started

1. Prerequisites

Platforms

The following prerequisites are required to make this repository work:

• AWS subscription
• Access to DagsHub
• Access to GitHub Actions

Other ressources

• Python 3.9.1
• DVC 2.11
• You can find all the additional information in the requirements.txt file

Main Components of Github Actions

GitHub Actions contains five main components as shown below.

Results On DagsHub

DagsHub provides the capabilities to use MLFlow and DVC while giving the choice of working on Github. The following results are the experiments from DagsHub, using MLFlow to track the model F1-Score, Precision and Recall.

Github Actions Experiment

The following animation corresponds to the execution of the pipeline using Github Actions from Data Extraction to Model Training.

Test App deployed on EC2

This is the final result after the model is deployed into productiion.

• Input:Using DagsHub and Github Actions is so cool.

• Response: Ham meaning that the previous message is not a spam :)

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