Machine learning projects are becoming increasingly complex, with large datasets, multiple models, and intricate pipelines. This complexity makes it difficult to keep track of changes to the project, and to reproduce experiments and results.
DVC (Data Version Control) is a tool that helps you manage the version control of your machine learning projects. It allows you to track changes to your code, data, and models, and to easily reproduce experiments and results.
- Data Management - Track and version large amounts of data along with your code, and use DVC as a build system for reproducible, data driven pipelines.
- Experiment Management - Easily track your experiments and their progress by only instrumenting your code, and collaborate on ML experiments like software engineers do for code.
- Model Management - Use the DVC model registry to manage the lifecycle of your models in an auditable way. Easily access your models and integrate your model registry actions into CICD pipelines to follow GitOps best practices.
It appears that Mlflow, Dagshub, and DVC share many similarities.
All of them are making daily progress, and determining superiority can be challenging. The choice depends on our specific use case or requirement.
The functionalities that I frequently utilize are as follows:
- MLflow : Experiment Tracking, Model deployment, and Model monitoring.
- Dagshub : Version control, and Centralized collaboration.
- DVC : Data versioning and execution pipeline with the help of directed acyclic graph (DAG).