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For this workshop you will require access to an Elastic Cluster with a Machine Learning node and the latest Sample Data. If you do not already have this, follow Lab 0 to set up a trial with the right configuration to be able to continue the workshop.
Step 1: Create your Elastic deployment
Step 1: Create your Elastic deployment
The easiest way to deploy an Elastic deployment is using Elastic Cloud. Log into your Elastic Cloud account. If you don't have an Elastic Cloud account, you can sign up for the Elastic Cloud Trial.
Let's create a deployment. To find all settings, click Edit settings:
Name: <any>
Cloud provider: <any>
Region: <any>
Hardware profile: General Purpose
Version: 8.9
Go ahead and click Create deployment.
3. In a few minutes your Elastic deployment will be ready!
⚠️ Make sure to save the deployment credentials.
That’s it!
4. Now that you are up and running, it’s time to get some data into Kibana. Kibana will open as soon as your deployment is ready, just click Continue.
Step 2: Add sample data
Step 2: Add sample data
Sample data sets come with sample visualizations, dashboards, and more to help you explore Kibana before you ingest or add your own data.
5. On the home page, click Try sample data.
6. Click Other sample data sets.
7. For all datasets, click Add data.
Step 3: Add a Machine Learning node to the cluster
Step 3: Add a Machine Learning node to the cluster
To be able to utilize Machine Learning, we need to have a node (or more) in the cluster with this role. Let's add one!
8. In Kibana, on the menu to the left, click on Manage this deployment.
This will take you to the Cloud Console.
9. Here, under Depoyments, click on the cluster name.
10. Select Actions, then click Edit Deployment.
11. When in Edit mode, click + Add capacity under Machine Learning instances, keep the default of 1 GB RAM in 1 zone.
12. Now you just need to click Save and then Confirm, and you are ready for next lab!
(click on Kibana and Open to get back to the Kibana!)
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