The DagsHub's Machine Learning and AI Challenge of Hacktoberfest 2023

Welcome to Hacktoberfest x DagsHub official contribution page!

DagsHub and Hacktoberfest invite you to engage, contribute, and level up your machine Learning skills by contributing 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.

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.
2. Make a Contribution: Fork the project on DagsHub under your name, solve an issue or make an enhancement the maintainers can benefit from.
3. Document Your Work: Maintain clear and concise documentation describing your work, processing steps, and any dependencies. This documentation is crucial for future users and should be reflected in the project’s README file.
4. Tag your project: Add relevant tags to the repository and files including 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 the 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.