In Cloudera Machine Learning on Private Cloud, the Machine Learning Workspace
provides a space for the data scientists' work. After your Administrator has created or
given you access to an environment, you can set up a workspace.
The first user to access the Machine Learning Workspace after it is created must
have the EnvironmentAdmin role assigned.
Log in to the Cloudera Data Platform Private Cloud web interface using your
corporate credentials or other credentials that you received from your Cloudera
Data Platform administrator.
Click Machine Learning Workspaces.
Click Provision Workspace. The Provision
Workspace panel displays.
In Provision Workspace, fill out the following
fields.
Workspace Name - Give the Machine Learning
Workspace a name. For example, test-cml. Do not use capital letters in
the workspace name.
Select Environment - From the dropdown, select
the environment where the Machine Learning Workspace must be
provisioned. If you do not have any environments available to you in the
dropdown, contact your Cloudera Data Platform administrator to gain
access.
Namespace - Enter the namespace to use for the
Machine Learning Workspace.
NFS Server - Select
Internal to use an NFS server that is
integrated into the Kubernetes cluster. This is the recommended
selection at this time.
The path to the internal NFS server is already set in the
environment.
In Production Machine Learning enable the following
features.
Enable Governance - Enables advanced lineage and
governance features.
Governance Principal Name - If
Enable Governance is selected, you can use
the default value of mlgov, or enter an alternative
name. The alternative name must be present in your environment and be
given permissions in Ranger to allow the Machine LearningGovernance
service deliver events to Atlas.
Enable Model Metrics - Enables exporting metrics
for models to a PostgreSQL database.
In Other Settings enable the following features.
Enable TLS - Select this to enable https access
to the workspace.
Enable Monitoring - Administrators (users with
the EnvironmentAdmin role) can use a Grafana dashboard to monitor
resource usage in the provisioned workspace.
Cloudera Machine Learning Static Subdomain - This is a custom
name for the workspace endpoint, and it is also used for the URLs of
models, applications, and experiments. Only one workspace with the
specific subdomain endpoint name can be running at a time. You can
create a wildcard certificate for this endpoint in advance. The
workspace name has this format: <static subdomain
name>.<application domain>
Click Provision Workspace. The new workspace
provisioning process takes several minutes.
After the workspace is provisioned, you can log in by clicking the workspace name on
the Machine Learning Workspaces page. The first user to log
in must be the administrator.
Test backing up of the Machine Learning Workspace. Ensure that the backup completes
successfully, and then ensure you have a process to back up the workspace at regular
intervals.