Open Source Experiment Tracking with MLflow 🐶

Welcome to DagsHub’s Experiment Tracking contribution project for Hacktoberfest 2023!

In this exciting Hacktoberfest challenge, DagsHub invites you to contribute MLflow experiment tracking capabilities to Open Source Machine Learning projects.

What is DagsHub?

DagsHub is a centralized platform to host and manage machine learning projects including code, data, models, experiments, annotations, model registry, and more! DagsHub does the MLOps heavy lifting for its users. Every repository comes with configured S3 storage, an experiment tracking server, and an annotation workspace - all using popular open-source tools like MLflow, DVC, Git, and Label Studio.

What's the Challenge?

DagsHub is excited to introduce the MLflow Experiment Tracking Contribution Challenge. In this challenge, we invite you to contribute MLflow experiment tracking capabilities to open-source projects on DagsHub. MLflow's experiment tracking capabilities allow data scientists and machine learning practitioners to log parameters, metrics, and trained models, facilitating comprehensive and reproducible experiments.

How Can You Participate?

Here's a step-by-step guide to get involved in this challenge:

1. Choose a Project: Explore open-source projects on DagsHub and select one that interests you. It can be any project that utilizes MLflow or would benefit from one.
2. Track Experiments with MLflow: Fork the project under your name and use MLflow to log parameters, metrics, and the trained model, as appropriate for the project's objectives.
3. Document Your Pipeline: Maintain clear and concise documentation describing your work, processing steps, and any dependencies. This documentation is crucial for future users and contributors and should be added to the project’s README file.
4. Tag your project: Add relevant tags to the repository and files including MLflow, experiment-tracking, hacktoberfest, and hacktoberfest-2023 labels to the DagsHub repository.
5. Submit Your Contribution: Open a Pull Request to the project on DagsHub.
6. Proof of Contribution: Open a Pull Request here with README file and a link to the project

Why Join the Challenge?

Participating in the DagsHub MLflow Experiment Tracking Contribution Challenge offers numerous benefits:

• Skill Enhancement: Hone your MLflow expertise and gain hands-on experience in implementing experiment tracking for machine learning projects.
• Collaborative Learning: Collaborate with open-source project maintainers and fellow contributors, expanding your network and knowledge.
• Contribution to Open Source: Contribute to the open-source community by enhancing the reproducibility and transparency of valuable machine learning projects.
• Visibility: Showcase your expertise to a wider audience within the data science and machine learning community.