Creating Your Project and Pipeline Server

As a preliminary step, each of you is going to:

🚀 Create a Data Science Project
- This will help keep your things organized and ready for action!
🌐 Create a Data Connection
- We need that for the pipeline server to store its artifacts.
🛠️ Deploy a Data Science Pipeline Server
- We will need one, and it’s better to create it from the start.
💻 Launch a Workbench
- We will use it to review content and notebooks and to run the lab exercises to optimize the model.
📥 Clone the Git Repo into Your Workbench
- This contains all the code from the prototype, ready for you to explore!

The instructions below will guide you through these steps. Follow them carefully.

🌟 Create a Project

First, in the OpenShift AI Dashboard application, navigate to the Data Science Projects menu on the left:

Figure 1. OpenShift AI Dashboard
Create a project with the same name as your user ID
- You have been assigned a unique user ID: userX
- You need to now create a project with the exact same name: userX
  
  🚨 Your assigned user is userX. Don’t mess that up or things will break later on!
Leave the resource name unchanged.
Optionally, enter your first and last name in the description of the project.
It should look like this:

Figure 2. Project

🚫 It should NOT be userX like in the screenshot. (for you, X should be a number instead)

🌈 Create a Data Connection for the Pipeline Server

We have deployed an instance of Minio in the cluster to act as a simple Object Storage for our purposes.
You will need to create a connection that points to it.

Figure 3. Connection
You need to select the connection type, in this case S3 compatible object storage -v1

Figure 4. S3 comaptible object storage
Here is the information you need to enter:
- Name:
  Minio - models
- Access Key:
  userX
- Secret Key:
  openshift
- Endpoint:
  http://minio-service.wksp-userX.svc.cluster.local:9000
- Region:
  none
- Bucket:
  userX
  🚨 Once again, the bucket you will use has to match with the user ID you were provided!
The result should look similar to:

Figure 5. Result

🛠️ Create a Pipeline Server

It is highly recommended to create your pipeline server before creating a workbench. So let’s do that now!

In your Data Science Pipeline (project userX), or in your Data Science Project, Pipelines, click on Configure Pipeline Server

Figure 6. Pipeline Server 1
Use the same information as in the Data Connection created earlier (Minio - models) and click the Configure Pipeline Server button:

Figure 7. Pipeline Server 2
When your pipeline server is ready, your screen will look like the following:

Figure 8. Pipeline Server 3

At this point, your pipeline server is ready and deployed. 🎉

There is no need for wait for the pipeline server to be ready. You can go now to the next steps and check this out later on, before Section 5 steps. This may take more than a couple of minutes to complete.