Models

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AI/ML Model Creation

Users can select an existing AI/ML Source Connector and use predefined harvesting strategies to dynamically populate input variables. This harvesting process is especially useful for utilizing Data Apps through no-code integration. After the AI/ML Model is created, users can develop visualizations and create Model Cards via Data Apps for further insights and analysis.

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Harvesting

Harvesting retrieves input parameters from business tools, essential for real-time machine learning model execution. Currently, we support harvesting input variables from sources such as CRM systems (e.g., Salesforce, Dynamics 365) and custom web applications.

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Harvesting Strategies:

• DOM (Document Object Model) Element Extraction: Harvest data from specific web page elements.
• Query Parameter Extraction: Extract data from URL query parameters.

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Data Types Supported:

• Text: Such as Customer names, product descriptions, etc.
• Images: Used for image recognition.
• Dynamic Variables: Values that change with user interactions.

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Integration with Model Creation

Harvesting is integrated into the model creation process, allowing users to define what data should be collected, ensuring real-time processing during model invocations.

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Preprocessing

Preprocessing is an important step that occurs after data harvesting and before model inference. It transforms and formats the harvested data to meet the specific input requirements of the machine learning model.

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Key Preprocessing Tasks:

• Parsing Specific Attributes: Extract and format the required data fields.
• Resizing Images: Ensure images are resized appropriately for image processing models.

These preprocessing steps ensure the data is properly prepared, optimizing accuracy and efficiency during real-time model inference.