HPE ML Ops Introduced
September 11, 2019
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Hewlett Packard Enterprise (HPE) announced a container-based software solution, HPE ML Ops, to support the entire machine learning model lifecycle for on-premises, public cloud and hybrid cloud environments.

The new solution introduces a DevOps-like process to standardize machine learning workflows and accelerate AI deployments from months to days.

The new HPE ML Ops solution extends the capabilities of the BlueData EPIC container software platform, providing data science teams with on-demand access to containerized environments for distributed AI / ML and analytics. BlueData was acquired by HPE in November 2018 to bolster its AI, analytics, and container offerings, and complements HPE’s Hybrid IT solutions and HPE Pointnext Services for enterprise AI deployments.

Enterprise AI adoption has more than doubled in the last four years1, and organizations continue to invest significant time and resources in building machine learning and deep learning models for a wide range of AI use cases such as fraud detection, personalized medicine, and predictive customer analytics. However, the biggest challenge faced by technical professionals is operationalizing ML, also known as the “last mile,” to successfully deploy and manage these models, and unlock business value. According to Gartner, by 2021, at least 50 percent of machine learning projects will not be fully deployed due to lack of operationalization.

HPE ML Ops transforms AI initiatives from experimentation and pilot projects to enterprise-grade operations and production by addressing the entire machine learning lifecycle from data preparation and model building, to training, deployment, monitoring, and collaboration.

“Only operational machine learning models deliver business value,” said Kumar Sreekanti, SVP and CTO, Hybrid IT at HPE. “And with HPE ML Ops, we provide the only enterprise-class solution to operationalize the end-to-end machine learning lifecycle for on-premises and hybrid cloud deployments. We’re bringing DevOps speed and agility to machine learning, delivering faster time-to-value for AI in the enterprise.”

“From retail to banking to manufacturing to healthcare and beyond, virtually all industries are adopting or investigating AI/ML to develop innovative products and services and gain a competitive edge. While most businesses are ramping up on the build and train phase of their AI/ML projects, they are struggling to operationalize the entire ML lifecycle from PoC to pilot to production deployment and monitoring,” said Ritu Jyoti, Program VP, Artificial Intelligence (AI) Strategies at IDC. “HPE is closing this gap by addressing the entire ML lifecycle with its container-based, platform-agnostic offering – to support a range of ML operational requirements, accelerate the overall time to insights, and drive superior business outcomes.”

With the HPE ML Ops solution, data science teams involved in building and deploying ML models can benefit from the industry’s most comprehensive operationalization and lifecycle management solution for enterprise AI:

- Model Build: Pre-packaged, self-service sandbox environments for ML tools and data science notebooks

- Model Training: Scalable training environments with secure access to data

- Model Deployment: Flexible and rapid deployment with reproducibility

- Model Monitoring: End-to-end visibility across the ML model lifecycle

- Collaboration: Enable CI/CD workflows with code, model, and project repositories

- Security and Control: Secure multi-tenancy with integration to enterprise authentication mechanisms

- Hybrid Deployment: Support for on-premises, public cloud, or hybrid cloud

The HPE ML Ops solution works with a wide range of open source machine learning and deep learning frameworks including Keras, MXNet, PyTorch, and TensorFlow as well as commercial machine learning applications from ecosystem software partners such as Dataiku and H2O.ai.

HPE ML Ops is generally available now as a software subscription, together with HPE Pointnext Services and customer support.

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