Create code recipes in the language of your choice, including Python, R, SQL, and more. When developing code directly in Dataiku, use the built-in code editor, the embedded Jupyter Notebook interface, or even code in an external IDE such as VS Code, PyCharm, Sublime Text, or R Studio. If you already have Jupyter Notebooks that have been developed outside of Dataiku, you can upload those Notebooks manually, connect to a remote Git repository, and use the typical branching, push, and pull actions to keep your code in Dataiku synced with that remote repository. Dataiku includes built-in algorithms from state-of-the-art machine learning libraries, such as Scikit-Learn, MLlib, and XGboost, plus TensorFlow and Keras for deep learning. But, you can also code your own custom models and still take advantage of all the benefits Dataiku Visual ML has to offer, such as automatic experiment tracking and diagnostics, interpretability and performance metrics, auto-documentation, and ease of version monitoring in production.