Monitoring models with OpenScale (Notebook)
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:
1. Open the notebook
If you Created the Project using the Customer-Churn-Project.zip file, your notebook will be present in that project, under the 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 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.
Notebook: openscale-full-configuration.ipynb
Notebook with output: openscale-full-configuration-with-output.ipynb
When the Jupyter notebook is loaded and the kernel is ready then we can start executing cells.
2. Update credentials
In the notebook section 1.2 you will add your ICP platform credentials for the
WOS_CREDENTIALS.For the
urlfield, changehttps://w.x.y.zto use the URL your ICP cluster, i.e something like:"url": "https://zen-cpd-zen.omid-cp4d-v5-2bef1f4b4097001da9502000c44fc2b2-0001.us-south.containers.appdomain.cloud".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!
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.
Last updated
