New, lightweight and customizable Cloudera ML Runtimes are now available in CDP Machine Learning and Cloudera Data Science Workbench 1.9. ML Runtimes have been rebuilt from the ground up to enable greater flexibility in frameworks, processing, and IDEs — powering customizable lightweight deployments without over encumbering the runtime profile. The new profiles are designed to meet the diverse needs of Data Scientists by enabling a variety of ML Runtimes natively and eliminating sizing and versatility issues with previous Cloudera Engine profiles.   ML Runtimes with version 2020-02 offer support for Python 3.6, 3.7, and 3.8 with both the Workbench editor and with JupyterLab (GA). All of these runtimes are offered as NVIDIA GPU edition with out-of-the-box GPU acceleration. New R 3.6 and 4.0 Runtimes are also available with the Workbench editor.    To learn more about our vision and roadmap read the Announcement blog post.   As always, we welcome your feedback. Please send your comments and suggestions on our community forums. ... View more

New, lightweight and customizable Cloudera ML Runtimes are now generally available in CDP Machine Learning and Cloudera Data Science Workbench 1.9. ML Runtimes have been rebuilt from the ground up to enable greater flexibility in frameworks, processing, and IDEs — powering customizable lightweight deployments without over encumbering the runtime profile. The new profiles are designed to meet the diverse needs of Data Scientists by enabling a variety of ML Runtimes natively and eliminating sizing and versatility issues with previous Cloudera Engine profiles.   This initial release ships with Python 3.6, 3.7, and 3.8 Runtimes as well as both the Workbench editor and with JupyterLab (Tech Preview). New Runtime options will soon follow!   To learn more about our vision and roadmap read the Announcement blog post.   Also in these releases:   CDP Machine Learning: Support scaling down to zero CPUs or GPUs on Azure Refreshed Data Science Project dashboard experience   CDSW 1.9: Availability of Shared Data Experience (SDX for models — enabling model governance and model cataloging on CDP Private Base. Refreshed Data Science Project dashboard experience LDAP Group Sync enables creating teams that synchronize with an LDAP group for easier user management.  Applications can now be configured for public, unauthenticated access. CDSW is now Certified with SLES12 SP5 and CentOS/RHEL 7.8   As always, we welcome your feedback. Please send your comments and suggestions on our community forums. ... View more

Model Monitoring & Deployment Security is now GA in CDSW 1.8. Available in this release is native MLOps functionalities for model monitoring, enabling tracking of individual model predictions down to the feature level for calculating model drift and ground truthing to production environments. Data scientists can analyze metrics using their preferred libraries and IDEs in any language, ensuring models are performing optimally and compliantly at scale.   Additionally, this release includes general availability of Resource Quotas and quota management. These features enable administrators to limit users’ aggregate CPU, memory, and GPU consumption to protect against over-usage resulting in critical compute resource shortages. Default quotas can be configured for a Workspace, and overridden on a per-user basis with Custom quotas.   Also in this release: Ability to use custom command-line arguments for jobs. Improved security for model deployments allowing user-level access controls to prevent unauthorized access of endpoints.  Read the release notes for the full list of smaller improvements and bug fixes.   Links: Download it here Upgrade with the Cloudera Manager Read the Production ML in CDSW blog CDSW Overview Getting started with CDSW   As always, we welcome your feedback. Please send your comments and suggestions on our community forums. ... View more