DVC

Step 1: Track the data used in the research with `dvc add`.

Step 2: Use `dvc exp run` to capture the code, parameters, and results of the experiment.

Step 3: Share the DVC repository with collaborators.

Step 4: Collaborators can use `dvc repro` to reproduce the experiment and verify the results.

Step 1: Define the data pipeline using `dvc.yaml`.

Step 2: Track the input data and output features with `dvc add`.

Step 3: Use `dvc repro` to execute the pipeline and transform the data.

Step 4: Integrate the pipeline with a CI/CD system for automated execution.

Step 1: Configure a remote storage location (e.g., S3, GCS) with `dvc remote add`.

Step 2: Track the dataset with `dvc add`, which creates a `.dvc` file.

Step 3: Commit the `.dvc` file to Git, but the data stays in remote storage.

Step 4: Use `dvc push` and `dvc pull` to transfer data to and from the remote storage.

Step 1: Each data scientist clones the DVC repository.

Step 2: They make changes to the code, data, or pipelines.

Step 3: They commit their changes to Git and push to the remote repository.

Step 4: DVC ensures that the data and models are synchronized across all collaborators.

Step 1: Track the ML model with `dvc add`.

Step 2: Use `dvc exp run` to capture the model's performance metrics.

Step 3: Commit the `.dvc` file and the experiment results to Git.

Step 4: Use DVC to compare different versions of the model and choose the best one for deployment.