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 Container Platform 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.
- Policy Management (Image Pull , Pod Security, Drift Detection) and Container Runtime Security - out-of-the-box such as Falco (open source only)
- RHEL 8 Support on GPU hosts (K8s hosts fresh install / K8s hosts upgrade (includes GPU support) and K8s Version 1.19.
- Model Management with ML Flow integration and Airflow Operator for Spark Scheduling.
- Upgrade Spark Operator to 3.0.1 – K8s only.
- Apache Livy for Apache Spark 3.
- Global FSMount - Enable sharing with Global FSMount across tenants
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.
Data scientists spend their time building models and analyzing results rather than waiting for training jobs to complete.
HPE Ezmeral Container Platform helps ensure no loss of accuracy or performance degradation in multi-tenant environments.
Increase collaboration and reproducibility with shared code, project and model repositories.
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.
Kubernetes ® is a registered trademark of the Linux Foundation in the United States and other countries, and is used pursuant to a license from the Linux Foundation. LINUX FOUNDATION and YOCTO PROJECT are registered trademarks of the Linux Foundation.
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