Conclusion¶

Congratulations! You did it!

In this first part, you were able to run a simple ML experiment with Jupyter Notebook, adapt and move the Jupyter Notebook to Python scripts, initialize Git and DVC for local training, reproduce the ML experiment with DVC and track model evolution with DVC.

The following diagram illustrates the bricks you set up at the end of this part.

flowchart TB
    dot_dvc[(.dvc)]
    dot_git[(.git)]
    data[data/raw] <-.-> dot_dvc
    workspaceGraph <-....-> dot_git
    subgraph cacheGraph[CACHE]
        dot_dvc
        dot_git
    end
    subgraph workspaceGraph[WORKSPACE]
        prepare[prepare.py] <-.-> dot_dvc
        train[train.py] <-.-> dot_dvc
        evaluate[evaluate.py] <-.-> dot_dvc
        data --> prepare
        subgraph dvcGraph["dvc.yaml (dvc repro)"]
            prepare --> train
            train --> evaluate
        end
        params[params.yaml] -.- prepare
        params -.- train
        params <-.-> dot_dvc
    end

Next steps¶

Ready to continue?

Proceed to Part 2 - Move to the cloud to learn how to move your ML workflow to cloud infrastructure.

Stopping here?

If you decide to conclude your progress at this point, see the Clean up guide for instructions on removing the resources you created:

• Local Git repository and DVC cache
• Python virtual environment
• Data files and model artifacts

This is necessary to return to a clean state on your computer and avoid potential issues when starting new projects.