DagsHub is a cloud-based platform that helps individuals and teams manage, track, and collaborate on machine learning (ML) projects. It provides a centralized platform for storing, tracking, and sharing ML models, data, and experiments. Additionally, DagsHub offers features for version control, resource management, and collaboration, making it easier for teams to work together on ML projects.

Some of the key features of DagsHub include:

• Centralized ML Project Management: DagsHub provides a single platform for managing all aspects of ML projects, including model development, training, and deployment.
• Version Control for ML Models: DagsHub allows users to track and manage different versions of their ML models, making it easy to compare and revert to previous versions.
• Resource Management: DagsHub helps users manage and allocate resources for ML training and experimentation. This includes managing compute resources, storage, and access to data.
• Collaboration: DagsHub enables teams to collaborate on ML projects by providing features for sharing models, data, and experiments. It also includes tools for tracking contributions and managing permissions.
• Experiment Tracking: DagsHub allows users to track and visualize experiments, including metrics, hyperparameters, and results. This helps users understand the performance of different models and identify the best performing configurations.

Overall, DagsHub provides a comprehensive platform for managing and collaborating on ML projects. It simplifies the process of developing, training, and deploying ML models, and makes it easier for teams to work together on ML projects.

Example

The integration of Dagshub with MLflow is highly effective.
You can find more about MLflow on my blog MLflow from my point of view

github repo mlflow-with-dagshub.

The first step is to create a project repository inside Dagshub or we can directly copy it from GitHub repo.

Before running our program we have to export/set some variables.

python example.py

It will take some time to execute.
After execution, we can check our experiments inside dagshub repo.