This section is broken up into the following steps:
Various parts of this workshop will require the attendee to upload files or run scripts that we've stored in the repository. So let's get that done early on, you'll need
git on your laptop to clone the repository directly, or access to GitHub.com to download the zip file.
To Download, go to the GitHub repo for this workshop and download the archived version of the workshop and extract it on your laptop.
Alternately, run the following command:
git clone https://github.com/IBM/cloudpakfordata-telco-churn-workshopcd cloudpakfordata-telco-churn-workshop
At this point of the workshop we will be using Cloud Pak for Data for the remaining steps.
Launch a browser and navigate to your Cloud Pak for Data deployment
NOTE: Your instructor will provide a URL and credentials to log into Cloud Pak for Data!
In Cloud Pak for Data, we use the concept of a project to collect / organize the resources used to achieve a particular goal (resources to build a solution to a problem). Your project resources can include data, collaborators, and analytic assets like notebooks and models, etc.
Go the (☰) menu and click Projects
Click on New project +
Select Analytics project for the project type and click on Next
We are going to create a project from an existing file (which contains assets we will use throughout this workshop), as opposed to creating an empty project. Select the Create a project from a file option:
Navigate to where you cloned this repository, then to
projects/ and choose
Customer-Churn-Project.zip. Give the project a name and click
After succesful creation, click on View new project
Cloud Pak for Data uses the concept of
Deployment Spaces to configure and manage the deployment of a set of related deployable assets. These assets can be data files, machine learning models, etc.
Go the (☰) menu and click
+ New deployment space:
Select the Create an empty space option.
Give your deployment space a unique name, optional description, then click
Create. You will use this space later when you deploy a machine learning model.
Next, we will add a collaborator to the new deployment space, so that assets we deploy can be monitored in the OpenScale model monitoring lab.
Click on the
Access control tab and then click on
Add collaborators + on the right.
Enter "admin" as a Collaborator and select the user from the drop down list. Then click on the
Add to list + button.
NOTE: We are adding the user that configured the machine learning instance for OpenScale monitoring. In this case, the user is the admin user.
Add button to finish adding the collaborator. You should be brought back to the deployment space page and see your user ID along with the
Admin user as collaborators for this space.