On-premises cloud experience accelerates data science

HPE GreenLake for ML Ops makes it easier and faster to get started with ML/AI projects and seamlessly scale them to production deployments. Within your datacentre or colocation facility, deploy AL/ML workloads on HPE's ML-optimised cloud service infrastructure featuring HPE Apollo hardware powered by HPE Ezmeral ML Ops – a solution that is designed to address all aspects of ML lifecycle, from data preparation to model building, training, deployment, monitoring and collaboration. HPE GreenLake offers consumption-based pricing, allowing you to consume these resources on premises with a cloud experience. 

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Solve for operational risk and data gravity issues

Avoid the compliance, security and data gravity issues of public cloud, and the operational risk of running the infrastructure yourself. Workloads run right next to your on-premises data lake letting you avoid hidden costs for data egress.

Empower data scientists

Let your data scientists focus on building models and not managing and configuring infrastructure. With HPE GreenLake for ML Ops, you can quickly spin up containerised ML/AI environments with your choice of data science tooling and assign projects to data scientists.

Enjoy elastic pricing and cost monitoring

Reserve the capacity you need and pay-per-use for the resources you consume. With the ability to view your metered usage and associated costs, you can tie usage to specific business objectives.

Secure provisioning and management

Offload monitoring and management of your Data Science environment. With HPE GreenLake for ML Ops, your environment is securely managed from HPE IT Operations Centres, and through HPE GreenLake Central security services.


HPE GreenLake for ML Ops

Run your ML workloads with the security and control that an on-premises infrastructure provides. Pick from 2 configurations – Standard and Performance Optimised – that are built on an enterprise-grade high-performing hardware/software stack optimised for machine learning. Featuring consumption-based billing, this service provides:

  • Simple, transparent pricing model delivering on-premises service as operational expense.
  • Elasticity to support unpredictable workloads.
  • Reserved capacity + usage-based consumption model drives pricing predictability while supporting variable demand typical of data science workloads.
  • 4 year contract, paid monthly.
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Standard Configuration
Performance Optimised Configuration

Who is this recommended for

Companies with a data science team who want to use artificial intelligence and machine learning to solve business problems and need to run ML/AI workloads in an agile and secure manner on premises, without having to manage the infrastructure.

Companies with a data science team who are training deep learning models at scale, putting models into production or are running multiple data science projects concurrently on premises.

Hardware specifications

  • Compute: HPE Apollo 6500 (6 CPUs, 96 usable CPU cores) integrated with accelerated NVIDIA Tesla V100 GPUs (4) and HPE ProLiant DL360 integrated with NVIDIA Tesla T4 GPUs (4). 
  • Storage:  HPE Apollo 4200 with 228TB of usable storage.
  • Compute: HPE Apollo 6500 (6 CPUs, 120 usable CPU cores) integrated with accelerated NVIDIA Tesla V100 GPUs (8) and HPE ProLiant DL360 integrated with NVIDIA Tesla T4 GPUs (4). 
  • Storage:  HPE Apollo 4200 with 394TB of usable storage and 150TB of NVMe storage.

Software stack

HPE Ezmeral Container Platform and ML Ops software, including a set of 5 pre-configured data science app images with the option to customise. These images contain a variety of open source data science tools, languages and CI/CD tooling and are designed for data ingestion, data prep, model training, model deployment and notebooks.

HPE Ezmeral Container Platform and ML Ops software, including a set of 5 pre-configured data science app images with the option to customise. These images contain a variety of open source data science tools, languages and CI/CD tooling and are designed for data ingestion, data prep, model training, model deployment and notebooks.

Control plane

Secure, self-serve provisioning and management via a common control plane for the HPE Container Platform and HPE GreenLake Central orchestration.

Secure, self-serve provisioning and management via a common control plane for the HPE Container Platform and HPE GreenLake Central orchestration.

What is metered

Usage is metered based on the compute (per minute) and storage (per GB) used by the nodes in a cluster.

There are 4 meters used to calculate usage over the reserved capacity.

  • CPU cores – usage by minute 
  • V100 GPU – usage by minute 
  • T4 GPU  - usage by minute 
  • Storage – GB average usage per hour

Usage is metered based on the compute (per minute) and storage (per GB) used by the nodes in a cluster.

There are 4 meters used to calculate usage over the reserved capacity.

  • CPU cores – usage by minute 
  • V100 GPU – usage by minute 
  • T4 GPU  - usage by minute 
  • Storage – GB average usage per hour

Incuded services

  • HPE engineers perform initial setup and integration with your datacentre infrastructure. Service includes proactive and reactive support, with a single point of contact.
  • The service includes several days of post install technical engagement with HPE experts. You can use this service at your discretion. 
  • Full monitoring and lifecycle management of the HPE GreenLake for ML Ops infrastructure by HPE.
  • HPE engineers perform initial setup and integration with your datacentre infrastructure. Service includes proactive and reactive support, with a single point of contact.
  • The service includes several days of post install technical engagement with HPE experts. You can use this service at your discretion. 
  • Full monitoring and lifecycle management of the HPE GreenLake for ML Ops infrastructure by HPE.