Conclusion¶

Congratulations! You did it!

In this second part, you were able to share your experiment on the cloud and with your peers. A new team member can easily clone the repository and reproduce the experiment locally. The experiment is also reproducible on the cloud and ensures it still works in a different environment. Once the experiment is reproduced, the results are published and shared with the team. You can also compare the results with the previous ones and decide if you want to merge the new model or not.

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

flowchart TB
    dot_dvc[(.dvc)] <-->|dvc push
                         dvc pull| s3_storage[(S3 Storage)]
    dot_git[(.git)] <-->|git push
                         git pull| gitGraph[Git Remote]
    workspaceGraph <-....-> dot_git
    data[data/raw] <-.-> dot_dvc
    subgraph remoteGraph[REMOTE]
        s3_storage
        subgraph gitGraph[Git Remote]
            direction TB
            repository[(Repository)] --> action[Action]
            action -->|dvc pull| action_data[data/raw]
            action_data -->|dvc repro| action_out[metrics & plots]
            action_out -->|cml publish| pr[Pull Request]
            pr --> repository
        end
    end
    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 3 - Serve and deploy the model of the MLOps guide!