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

Do not forget to Clean up if you want to stop here or continue with Part 2 - Move to the cloud of the MLOps guide!