HPE Ezmeral Machine Learning Ops

Much like pre-DevOps software development, data science organizations still spend a significant amount of time and effort when moving projects from development to production. Model version control and code sharing is manual, and there is a lack of standardization on tools and frameworks, making it tedious and time-consuming to productize machine learning models.

HPE Ezmeral Machine Learning Ops (HPE Ezmeral ML Ops) extends the capabilities of the HPE Ezmeral Runtime Enterprise and brings DevOps-like agility to enterprise machine learning. With the HPE Ezmeral ML Ops, enterprises can implement DevOps processes to standardize their ML workflows.

HPE Ezmeral ML Ops provides data science teams with a platform for their end-to-end data science needs with the flexibility to run their machine learning or deep learning (DL) workloads on-premises, in multiple public clouds, or a hybrid model and respond to dynamic business requirements in a variety of use cases.

• KubeFlow 1.3 (security hardened)? and Model Monitoring

• Spark Operator Add-on, Spark History Server, Spark Thrift Server, Apache Livy and Hive Metastore

• Delta Lake support

• Spark versions supported: Apache Spark 2.4.7 and Apache Spark 3.1.2

• HPE Ezmeral Runtime Analytics for Apache Spark

• Introducing HPE Ezmeral Runtime Enterprise New UI with ?Enhanced UX: improved UX for data scientists?; Packaged libraries for simplified coding experience and Notebook magics for KubeFlow

Faster Time to Value

Manage and provision infrastructure through an intuitive graphical user interface.

Provision development, test, or production environments in minutes as opposed to days.

Onboard new data scientists rapidly with their choice of tools and languages without creating siloed development environments.

Improved Productivity

Data scientists spend their time building models and analyzing results rather than waiting for training jobs to complete.

HPE Ezmeral Runtime Enterprise helps ensure no loss of accuracy or performance degradation in multi-tenant environments.

Increase collaboration and reproducibility with shared code, project and model repositories.

Reduced Risk

Enterprise-grade security and access controls on compute and data.

Lineage tracking provides model governance and auditability for regulatory compliance.

Integrations with third-party software provides interpretability.

High-availability deployments help ensure critical applications do not fail.

Flexible and Elastic

Deploy on-premises, cloud, or in a hybrid model to suit your business requirements.

Autoscaling of clusters to meet the requirements of dynamic workloads.