Getting Started with AI Workbench

AI Workbench combines the power of machine learning with the rich profile data in BlueConic. BlueConic users with the “AI Workbench” permission enabled (Settings > Roles) can use AI Workbench to apply machine learning models to BlueConic profiles and data.

  1. To get started, select AI Workbench from the BlueConic navigation bar.
    machine learning and AI with BlueConic AI Workbench
    AI Workbench UI opens, and here is where you add, delete, and manage Jupyter notebooks.
  2. Select Add notebook and choose a new notebook type, for example "API examples notebook" to get started. These examples contain sample Python code, and also a basic simple notebook with some visualizations.
    Getting Started with Jupyter notebooks for machine learning and AI BlueConic CDP
  3. Provide a name for your notebook at the top of the page.
    If you're familiar with Jupyter, you'll recognize the menu, toolbar commands, and code cells from the notebook interface. See the AI Workbench API page for reference documentation.
    Here's an example notebook showing an application that calculates customer lifetime in days, based on profile properties that store first and last visit dates.
    customer lifetime value (CLV) machine learning modeling with BlueConic CDP
    Saving and deleting notebooks: Use the Save menu to save, save as, or delete a notebook in AI Workbench.

    Adding parameter values to a model: Marketing teams can add parameter values to a notebook in the UI on the Parameters panel without having to edit or write code.

    Running notebook code: To execute the selected notebook cell, select Run on the notebook toolbar, press Shift+Enter, or use the run commands in the Schedule and run history panel. Learn more about scheduling AI Workbench notebooks.

    Kernels: The Jupyter notebooks you create inside the BlueConic AI Workbench are connected to a kernel through the Jupyter UI.  A Python3 kernel executes the notebook code and returns results. The kernel and the notebook remain active while you are actively working in them. The kernel will be automatically terminated after 36 hours of inactivity, or when a BlueConic user manually terminates them. Also, a kernel will be automatically terminated when a notebook is opened without running one or more cells (once you navigate to another notebook).

    Privacy management: In the right-hand side bar, you can see a list of the privacy and consent management Objectives and other related items for this notebook. Select Add to objective to add this notebook in an existing privacy objective.

  4. Return to AI Workbench to see the list of notebooks.
    In the list of Jupyter notebooks, each notebook icon shows the AI Workbench notebook's status. The table below describes what each notebook icon means.
  5. To see all the running kernels, select Running notebook kernels from the Save menu in AI Workbench.
    running Jupyter notebook kernels AI Workbench BlueConic
    The Running notebook kernels window appears, showing all running notebooks and the amount of memory in use and free.
    running Jupyter notebooks for machine learning and AI BlueConic CDP
    Memory: At the bottom of the window, you can see how much memory is available and in use. You need at least 100MB of RAM available to start a new notebook and kernel.
  6. To terminate a kernel, select its name, and select Terminate kernel(s).
    Jupyter kernels that have been idle for 36 hours will automatically be terminated.

Next steps

Share your results using dashboards and insights. See the Notebook - All cells insight and Notebook - Single cell insight to visualize your AI Workbench results.