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

Next steps¶

Ready to continue?

Proceed to Part 3 - Serve and deploy to learn how to serve your model in production and deploy it to Kubernetes.

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
• Cloud storage bucket (S3/GCS)
• CI/CD pipeline configurations
• Remote Git repository resources

This is necessary to return to a clean state on your computer, avoid incurring unnecessary costs, and address potential security concerns when using cloud services.