Monitoring models with OpenScale (Notebook)

There are several ways of configuring Watson OpenScale to monitor machine learning deployments, including the FastPath automatic configuration, using the GUI tool, a more manual configuration using the APIs, and some combintation of these. For this exercise we're going to configure our OpenScale service by running a Jupyter Notebook. This provides examples of using the OpenScale Python APIs programatically.

This lab is comprised of the following steps:

1. Open the notebook

If you Created the Project using the file, your notebook will be present in that project, under the Assets tab:

Project from zip assets tab

You may now skip to the next step Update credentials

Import the notebook (If you are not using the Project Import pre-work steps)

NOTE: You should probably not need this step, and should only perform it if instructed to.

If, for some reason, you have not followed the Create a Project and Deployment Space step in the Pre-work to import, then you will need to import the notebook file by itself. Use the following steps for that.

At the project overview click the Add to project button, and choose Notebook or click New notebook option next to the Notebooks section.

Add a new asset

On the next panel select the From URL tab, give your notebook a name, provide the following URL, and choose the Python 3.6 environment:

The notebook is hosted in the same repo as the workshop.

Add notebook name and URL

When the Jupyter notebook is loaded and the kernel is ready then we can start executing cells.

Notebook loaded

2. Update credentials

  • In the notebook section 1.2 you will add your ICP platform credentials for the WOS_CREDENTIALS.

  • For the url field, change https://w.x.y.z to use the URL your ICP cluster, i.e something like: "url": "".

  • For the username, use your login username.

  • For the password, user your login password.

3. Run the notebook

Important: Make sure that you stop the kernel of your notebook(s) when you are done, in order to prevent leaking of memory resources!

Stop kernel

Spend an minute looking through the sections of the notebook to get an overview. You will run cells individually by highlighting each cell, then either click the Run button at the top of the notebook. While the cell is running, an asterisk ([*]) will show up to the left of the cell. When that cell has finished executing a sequential number will show up (i.e. [17]).

4. Get transactions for Explainability

Under 8.9 Identify transactions for Explainability run the cell. It will produce a series of UIDs for indidvidual ML scoring transactions. Copy one or more of them to examine in the next section.