Recipes

A collection of recipes is used to build a machine learning model. Each recipe consists of a series of transformations that are applied to a dataset. These transformations perform specific actions at different stages of the model-building process. You can run these recipes on an uploaded dataset to achieve the desired transformations. After running a recipe, the transformed output is generated, which could be a dataset or a chart. You can then apply additional recipes to this output, continuing the process until you achieve the desired result.

Recipe Types

The platform provides users with four distinct types of recipes to build and transform machine learning models:

AI-Assisted Recipe: This type of recipe leverages the power of artificial intelligence to assist users in creating code-based solutions. Users can either allow the AI to generate the code automatically or write their own custom logic using Python. This feature is particularly useful for those who want to develop complex transformations or model-building steps with the help of AI.
Rapid Model Recipe: This option is designed for users who may not have coding expertise. Without the need for programming or using advanced recipe types, users can quickly build machine learning models with just a few clicks. The Rapid Model recipe simplifies the process, allowing even non-technical users to develop data pipeline and thereby models efficiently.
Template Recipe: The Template recipe allows users to apply predefined templates for data transformation and model-building tasks. These templates are pre-configured to handle common data transformation operations, streamlining the process for users who need ready-made solutions for their datasets.
Code Recipe: This recipe type is designed for users who wish to write their own Python code. By writing Python code, users can create or manipulate datasets by connecting to external services, running recipes on the dataset, and generating the desired outcomes.