Welcome to the MLOps Repository!

Introduction

This repository is a hub for learning and sharing knowledge on Machine Learning Operations (MLOps). It serves as a collaborative space for code and insights related to machine learning models.

What is MLOps?

MLOps, or Machine Learning Operations, refers to the practices and tools designed to deploy, monitor, and manage machine learning models efficiently in production. It's an interdisciplinary field that merges DevOps, data engineering, and machine learning principles to facilitate the seamless development and deployment of ML systems.

Repository Structure

• Master Branch: The main branch includes this README and general information about the repository.
• Machine Learning Branch: Contains Jupyter notebooks, Python scripts, and datasets for training and evaluating ML models.

How to Contribute

1. Fork the Repository: Start by forking the repository to your GitHub account.
2. Clone Locally: Clone your forked repository to your local machine using git clone.
3. Create a Branch: For new features or fixes, create a new branch from master.
4. Commit Changes: Make your changes and commit them with a clear, descriptive message.
5. Push to GitHub: Push your branch and changes to your GitHub repository.
6. Open a Pull Request: Submit a pull request to the original repository for review.

Get Involved

We welcome contributions from everyone! Whether you're fixing bugs, adding new features, or improving documentation, your help is appreciated. Check out the issues for ideas on where to start.

Thank you for being a part of our MLOps journey!