Within the AI & Analytics Engine, a recipe is a sequence of data transformation actions
The recipe function on the AI & Analytics Engine supports the need for preparing datasets into an analysis-ready or ML-ready form. A recipe is specified as a chain of data transformations that is applied to a dataset to prepare it. Each transformation is called an “action” in the recipe.
For more information, read this article.
Recipes are created on the Engine either manually by users or automatically by the Engine’s template processor, depending on the type of application the user is working with:
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While creating apps using the “build from scratch” option, users can create custom recipes manually and run them to prepare their datasets using the user-friendly Recipe Editor.
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While creating template-based apps (such as customer-churn prediction), the Engine processes the business requirements provided by the user as template inputs, to automatically create appropriate recipes and run them.
On the Engine, recipes can be created to cover many aspects of data preparation (also known as data wrangling / or data munging), such as:
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Data cleaning and normalization
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Reshaping
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Joining multiple datasets
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Aggregating data at different levels
Note: To understand what actions are available in our Engine read this product documentation article: Actions Catalogue.
With a recipe, users can prepare their dataset in a systematic and repeatable way. Each recipe is reproducible, meaning that it can be applied to different datasets with a compatible schema.
When an action is added, it forms part of the recipe. Actions can be added/removed/modified/re-configured as long as the recipe is in the “Editable” state. In the recipe editor, actions are applied a sample of the dataset to which the recipe is to be applied, giving users a preview of the prepared dataset.
Once the user is satisfied with a recipe, they can apply it on the full dataset to generate the output dataset. The recipe can no longer be modified. However, the user can copy the recipe into another modifiable recipe to apply it on the same or a different dataset.