MLOps-Basics

This project is a basic introduction to the features of MLflow. It's designed to help me understand how MLflow works and how it can be used in machine learning projects.

Project Overview

In this project, I've set up a simple machine learning workflow to explore the features of MLflow. The goal wasn't to follow a rigorous data science workflow, but rather to get things running and see how it goes.

Features Explored

Here are some of the MLflow features I've explored in this project:

1. Tracking: I've used MLflow's tracking feature to log parameters, metrics, and artifacts. This helped me understand how MLflow can be used to keep track of different experiments in a systematic way.

2. Projects: I've set up this project as an MLflow project, which helped me understand how MLflow can be used to package machine learning code in a reusable and reproducible way.

3. Models: I've used MLflow's model feature to save and load models. This helped me understand how MLflow can be used to manage models and their versions.

Running the Project

To run this project, you need to have Python and MLflow installed on your machine. Once you have these prerequisites, you can follow these steps:

1. Clone the repository to your local machine.
2. Navigate to the project directory in the terminal.
3. Run the main script with the command: python main.py
4. After running the script, start the MLflow tracking server with the command: mlflow ui
5. Open a web browser and go to http://localhost:5000 to view the MLflow UI and see the results of your script.

Please note that this project is a basic introduction to MLflow and does not follow a rigorous data science workflow.

Future Work

I plan to explore more advanced features of MLflow and use it in more complex machine learning projects. I also plan to follow a more rigorous data science workflow in my future projects.