What’s Next?

Congratulations! In a short amount of time, you learned how Dataiku enables data scientists and coders through the use of:

• Python notebooks for EDA, creating code recipes, and building custom models;

• code libraries for code-reuse in code-based objects;

• the visual ML tool for customizing and training ML models; and

• scenarios for workflow automation.

You also learned about code environments and how to create reusable data products. To review your work, compare your project with the completed project in the Dataiku Gallery.

Your project also does not have to stop here. Some ways to build upon this project are by:

• Documenting your workflow and results in a wiki;

• Creating webapps and Dataiku applications to make assets reusable;

• Sharing output datasets with other Dataiku DSS projects or using plugins to export them to tools like Tableau, Power BI, or Qlik.

Finally, this quick start is only the starting point for the capabilities of Dataiku DSS. To learn more, please visit the Dataiku Academy, where you can find more courses, learning paths and can complete certificate examinations to test your knowledge.