PokeMLOps

PokeMLOps's main objective is to provide a comprehensive and user-friendly MLOps platform by integrating powerful open-source tools that enable easy management, deployment, and evaluation of machine learning models.

Description

This project delivers an efficient MLOps solution for managing and deploying ML models with ease, applied to a Pokemon Generation One classifier. The model's performance and robustness is taken into consideration to ensure that it can handle noise, data transformations, and data drift. In addition to this, the project has a monitoring system that tracks various metrics using Open Telemetry. The project also uses MLFlow to track experiments, manage models, and create projects.

Setup

1. Clone the repository

git clone https://github.com/Fatma-Chaouech/PokeMLOps.git

2. Install conda
3. Navigate to the project directory

cd PokeMLOps

4. Create and activate the environment

conda create --name pokenv
conda activate pokenv
conda env update --file environment.yml

5. Pull the dataset

dvc pull

Usage

1. Open MLFlow UI to track the experiments

mlflow ui

2. Visualize the pipeline

dvc dag 

3. Run the pipeline

dvc repro

If you wan to run a specific stage, run the same command followed by the name of the stage: dvc repro STAGE

There are default values for the arguments.