Previously, the settings on a Cloudera Machine Learning (CML) Workspace created on Microsoft Azure could not be adjusted after the ML Workspace was provisioned. This meant that, for example, the autoscaling range for Azure instances could not be changed in order to provide additional compute to the Users on the ML Workspace.   When viewing the details of a given ML Workspace from the CDP Management Console, several settings are now updateable. These include the Allowed Load Balancer Source Ranges, API Server Authorized IP Ranges, and Autoscale Range on the Azure instances. This enables Admins to not only enable the ML Workspace to continue to work even after networking changes, but also either expand or restrict the number of instances that could be made available to handle the various workloads on the ML Workspace. ... View more

While Cloudera has documented the prerequisites necessary to provision a new Cloudera Machine Learning (CML) Workspace in the Public Cloud, steps can be missed which lead to a provisioning failure. Sometimes, these failures do not appear until minutes after a provisioning action has begun, which can lead to frustration in getting CML set up.   After an Admin provides the necessary settings and clicks the “Provision Workspace” button, CML will immediately conduct a Validation Check of the provided inputs. Any issues found will be highlighted on the “Provision Workspace” screen, enabling Admins to correct any issues before attempting to provision the ML Workspace again. ... View more

Applied ML Prototypes (AMPs) in Cloudera Machine Learning (CML) enable Users to immediately get started with powerful end-to-end examples of Projects that include all the dependencies, algorithms, and user applications required to directly apply use cases to their own needs. While CML ships with access to a library of AMPs created and maintained by Cloudera’s own Fast Forward Labs team, Admins can now set up customized AMPs that are stored in Azure Repos.   When defining the AMP Catalog Sources, Admins have been able to define sources through a Git Repository URL or Catalog File URL, pointing at repositories internal to the organization. Admins can now point these sources to repositories stored in Azure Repos, in addition to other repositories such as GitHub. To learn more, visit the documentation about AMP Catalogs. ... View more

ML Runtimes are designed to be the successor to Engines in Cloudera Machine Learning (CML), moving away from the monolithic architecture of Engines and enabling finer grained control over exactly what is necessary by defining the Editor, Kernel, Edition, and Version needed for the given workload. One core piece previously missing from ML Runtimes was the ability to define Addons that go beyond these settings, in particular the ability to leverage the Spark execution engine through an ML Runtime.   Admins can now define Addons for ML Runtimes, including Spark and Hadoop CLI. In doing so, Users can leverage these Addons when running workloads such as Sessions, thereby enabling such functionality as Spark-on-K8s to be run through the ML Runtime. To learn more, visit the documentation about Runtime Addons in CML. ... View more

Previously, Admins could assign roles such as “MLUser” at the Environment level, enabling the Users to access the ML Workspaces under that Environment. However, those users would not show up inside the ML Workspace until they log in for the first time, preventing other Users from taking actions such as proactively adding them to Projects.   From the Users page, Admins can now sync the list of Users between the CDP Management Console and the given ML Workspace. After doing so, any users who have been assigned access to the ML Workspace through the “MLAdmin”, “MLUser”, or “MLBusinessUser” role would appear appropriately within the ML Workspace. This enables other Users to manage any new Users who have not logged in yet as needed, such as adding them to necessary Teams, Projects, etc. To learn more, visit the documentation about Synchronizing Users in CML. ... View more

Business Users, who are meant to use and consume information from Applications deployed in Cloudera Machine Learning (CML), can now be enabled with a streamlined user experience that not only optimizes their access to the Applications, but also unintrousively limits their ability to access workloads that they do not have access to in CML.   Admins can now define Business Users by applying the “MLBusinessUser” role at the Environment level, enabling the Business Users to access the ML Workspaces under that Environment. When viewing a given ML Workspace, Business Users will only see the Applications that have been assigned to them through Projects  To learn more, visit the documentation about Business Users in CML. ... View more