OpenScale Manual Config Part 3
Last updated
Last updated
Watson OpenScale utilizes several monitors to gather data about machine learning inferences and the GUI tool can then present that data in a form that is useful. In this sub-module we will use a Jupyter notebook to configure the monitor for Quality and enable Feedback logging.
The submodule contains the following steps:
If you Created the Project using the Customer-Churn-Project.zip file, your notebook will be present in that project, under the Assets tab:
TODO: Replace with new image
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 are not using the Created the Project 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 New Asset button, and choose Add notebook.
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-quality-feedback.ipynb
Notebook with output: openscale-fairness-explainability-with-output.ipynb
When the Jupyter notebook is loaded and the kernel is ready then we can start executing cells.
In the notebook section 2.0 you will add your Cloud Pak for Data 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": "https://zen-cpd-zen.omid-cp4d-v5-2bef1f4b4097001da9502000c44fc2b2-0001.us-south.containers.appdomain.cloud".
For the username, use your Cloud Pak for Data login username.
For the password, user your Cloud Pak for Data 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. [17]).
We've enabled the monitor for Quality and Feedback logging, now let's explore the results in the OpenScale GUI.
In the same browser (but a separate tab), open the Services tab by clicking the icon in the upper right. Go to the OpenScale tile under the AI category and click Open:
When the dashboard loads, Click on the 'Model Monitors' tab and you will see the deployment you configured in the jupyter notebook when you ran it in the previous section:
Do not worry if the name you see does not match exactly with the screenshot. The deployment name you see will correspond to the variable used in the Jupyter notebook
In our dashboard we can see that we have a choice for a variety of graphs under Quality. If we choose Area under ROC, where there is a threshold violation in my example, we'll see a limited chart due to the lack of scoring data. (More data will be added later to make this more interesting.
Click on a time slot to dig deeper into the graph:
We can see statistics for this time slot including Area under ROC, TPR, FPR, Recall, Precision, and more:
Other time slots can be examined to gather the relevant quality statistics.
Payload logging is enabled and will take place automatically when used with Watson Machine Learning. All Scoring request payloads and the returned data will be logged in the datamart.
In this sub-module we've setup Payload logging and the Quality monitor. Move on to the next submodule to learn about the Drift monitor
TODO: new image
