Setting AI Workbench notebook parameters

In the BlueConic AI Workbench, non-technical users can update the inputs or parameters to an AI model and run the model without writing any code. For example, if you're running an AI model to calculate customer lifetime value or RFM scoring model based on BlueConic profile properties, your marketing teams can easily supply these values in the Parameters tab of AI Workbench, save their settings, and run the model.

How do you set input parameters for AI Workbench models?

In AI Workbench applications, models typically call for customer profile or event data as input. Marketers can use dropdown menus or text boxes in the Parameters tab of AI Workbench to supply the input parameters for running AI models against customer profiles, without writing any code.

AI-Workbench-Easy-Editing-Marketing-Users.png

How do you create notebook parameters?

BlueConic provides prebuilt AI notebooks that nontechnical teams can use to run AI models. For custom models, data scientists and developers can create models in Python code in the built-in Jupyter notebook environment. For these custom models, developers specify which parameters are used in the code, and they provide parameters name. For example, the model might use a segment, and marketing teams supply the values they want to use, for example, "Blog Subscribers." The developer would add the following code to the notebook to create a parameter "Customer segment" for the marketing teams to edit:

# Specify parameters 
segment_id = bc.get_blueconic_parameter_value('Customer segment', 'segment')
customer_segment = bc.get_segment(segment_id)

Calling the function get_blueconic_parameter_value() will create the parameter for the marketing team to edit.

How do I set machine learning ai marketing parameters for the machine learning marketing models in BlueConic CDP AI Workbench?

What types of BlueConic values can be a notebook parameter?

Valid parameter types include these types of BlueConic data: channel, connection, date, datetime, dialogue, external_tracker, int (for integer), listener, objective, notebook, profile_property, profile_property_unique, segment, str (for string), and text. For the full list, see the Notebook Parameters API.

How do you edit notebook parameters? 

How do I set ai marketing parameters for marketing machine learning models in BlueConic CDP AI Workbench?

To edit parameters for an AI Workbench application:

  1. In AI Workbench, open the Jupyter notebook that contains your application.
  2. Select the Parameters tab and choose values to supply data to the application.
    For example, in the Customer segment field, the application calls for a BlueConic segment. We chose Blog Subscribers for this value.
  3. Continue selecting values as called for in your application.
  4. Save your settings.
    The next time this notebook runs, it will use the values you supplied.