Major features and updates for the Cloudera Machine Learning service on Private Cloud.
April 27, 2021
CML on Private Cloud, version 1.2, has the following new features and updates.
- Support for OCP 4.6 and upgrading from PVC 1.1.
- Improved non-transparent proxy support for air-gapped environments.
- Introduced Applied ML Prototypes (AMPs).
- Added NFS support:
- NFS versions v3 and v4.x are supported.
- External NFS security improvements -
no_root_squashexport option has been removed.
- Support added for custom service principals (Beta).
- API v2 (Beta). This is the beta release of a supported API for CML. As it is in beta, it is subject to change without warning before becoming GA.
- Monitoring now uses CDP centralized Grafana. Added database metrics and improved alerts.
- DSE-12037 - Fixed an issue with the seamless login for Grafana.
- DSE-14891 - Fixed an issue with broken Engine and Session log links.
- Various security fixes.
December 16, 2020
CML on Private Cloud, version 1.1, has the following new features and updates.
- MLOPS-216 - Production ML Support
Model Metrics track machine model serving performance metrics. Model Governance use Apache Atlas to track builds, experiments and deployment of machine learning models.
- DSE-10777 - UMS Integration
MLUser and MLAdmin resource roles are now available and assignable through Environment settings.
- DSE-12955 - Self Signed Private CA certs For custom container registries
Customers can now use Container registries that are using self signed or private CA signed certificates. There is an option to upload the self signed or private CA signed certificates certificate during Private Cloud installation.
- DSE-10759 - GPU support
The OpenShift Nvidia operator is now supported for use with CML workloads.
August 17, 2020
This is the first release of CML on Private Cloud, version 1.0.
- Run Machine Learning workloads on OpenShift clusters in your own data center.
- Easily onboard a new tenant and provision an ML workspace in a shared OpenShift environment.
- Enable data scientists to access shared data on CDP Private Cloud Base and CDW.
- Leverage Spark-on-K8s to spin up and down Spark clusters on demand.
- Take advantage of most CML features on public cloud, including Teams, Projects, Experiments, Models, and Applications.