HPE S0M22AAE - Ezmeral ML Ops 5yr 24x7 E-LTU

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.

What is new supporting point:
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
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