There are several ways of configuring Watson OpenScale to monitor machine learning deployments, including the 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:
You may now skip to the next step Update credentials
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 Customer-Churn-Project.zip, 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.
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.
When the Jupyter notebook is loaded and the kernel is ready then we can start executing cells.
In the notebook section 1.2 you will add your ICP platform credentials for the
url field, change
https://w.x.y.z to use the URL your ICP cluster, i.e something like:
username, use your login username.
password, user your login password.
Important: Make sure that you stop the kernel of your notebook(s) when you are done, in order to prevent leaking of memory resources!
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.
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.