DL project tracking with MLflow and DVC
MLflow is a popular framework that supports tracking technical dependencies, model parameters, metrics, and artifacts. The key components of MLflow are as follows:
- Tracking: It keeps a track of result changes every time the model runs
- Projects: It packages model code in a reproducible way
- Models: It organizes model artifacts for future convenient deployments
- Model Registry: It manages a full life cycle of an MLflow model
- Plugins: It can be easily integrated with other DL frameworks as it provides flexible plugins
As you may have already noticed, there are some similarities between W&B and MLflow. However, in the case of MLflow, every experiment is linked with a set of Git commits. Git does not prevent us from saving datasets, but it shows many limitations when the datasets are large, even with an extension built for large files (Git LFS). Thus, MLflow is commonly combined with DVC, an open source version control...
