HPE EZMERAL ML OPS

A solution that brings DevOps-like agility to the entire machine learning lifecycle.

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Operational machine learning at enterprise scale

Bring DevOps-like speed and agility to ML workflows with support for every stage of the machine learning lifecycle: from sandbox experimentation with your choice of ML/DL frameworks, to model training on containerised distributed clusters, to deploying and tracking models in production.

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Just the Facts with HPE Ezmeral ML Ops

How do we fast forward to the future of data science? Meet HPE Ezmeral ML Ops, which accelerates model deployment from years to months and is available as a service. It’s time to unlock the potential of your data and realise the possibilities of machine learning.

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A CONTAINER-BASED SOLUTION FOR THE ML LIFECYCLE

Standardise processes across the ML lifecycle to build, train, deploy and monitor machine learning models.

Model Build

Containerised sandbox environments with a variety of machine learning and deep learning applications and tools

Model Training

Scalable training environments with secure access to big data

Model Deployment and Monitoring

Flexible and scalable endpoint deployment with end-to-end monitoring

Collaboration

Enable DevOps-like processes with code, model and project repositories

Security and Control

Secure multi-tenancy with integration to enterprise authentication mechanisms

Hybrid Deployment

Build, train and deploy models either on premises, in the cloud or in a hybrid model

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increased profitability ¹
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better customer experience ²
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better adoption of data science best practices ³

HPE Discover Virtual Experience - Fuel data-driven transformation with HPE Ezmeral Cloud Containers

Now available on-demand, see how to run, manage, control and secure apps, data and IT operations that run your business. Learn how HPE Ezmeral software runs containers and Kubernetes at scale to modernise apps, from edge to cloud.

Technical videos

Build and Train a Model with HPE ML Ops

Deploy the Model with HPE ML Ops

Setup Project Repository in HPE ML Ops

Operationalising the ML Lifecycle

HPE Ezmeral ML Ops: Overview of the entire machine learning lifecycle

Operationalisation for the ML lifecycle demo

Using HPE Ezmeral ML Ops to drive success with AI

Bringing DevOps to machine learning

Machine Learning with Symbolic Data

Machine Learning with Symbolic Data - Extended

“Our online games generate billions of data points every day. Using complex ML models, our data scientists leverage this data for prescriptive analytics to improve our players’ experience, lifetime value and loyalty. With HPE’s BlueData software, we’re containerising these ML and analytics environments to help improve operational efficiency and optimise our business.”

Alex Ryabov, Head of Data Services, Wargaming
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“As a healthcare company, we have vast amounts of data, but it’s been difficult for data science teams to get access to that data and operationalise their analytics and machine learning pipelines. BlueData has helped us to address these challenges with their containerised solution that has delivered faster time-to-insights, reduced our costs and freed up our staff to innovate. It’s paying big dividends for our organisation, and we look forward to continuing our journey together.”
Ramesh Thyagarajan, Executive Director, The Advisory Board Company (Optum)
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“While most businesses are ramping up on the build and train phase of their AI/ML projects, they are struggling to operationalise the entire ML lifecycle from PoC to pilot to production deployment and monitoring. 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.”
Ritu Jyoti, Program Vice President, AI Strategies at IDC
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FASTER TIME TO VALUE FOR AI / ML

HPE provides data science teams with one-click deployment for distributed AI / ML environments and secure access to the data they need.

ML Solution

HPE Ezmeral ML Ops

A software solution that extends the capabilities of the HPE Ezmeral Container platform to support the entire machine learning lifecycle and implement DevOps like processes to standardise machine learning workflows.

Containers

HPE Ezmeral Container Platform

Software platform designed to run both cloud-native and non-cloud native applications in containers.

Benefits

Transform non-cloud native apps without re-architecting.

Build an application once and deploy anywhere.

Proven enterprise-class security, performance and reliability.

ML Solution

HPE Ezmeral ML Ops

A software solution that extends the capabilities of the HPE Ezmeral Container platform to support the entire machine learning lifecycle and implement DevOps like processes to standardise machine learning workflows.

SUCCESS IN ACTION

ADVISORY BOARD (OPTUM) USES BLUEDATA TO ACCELERATE BUSINESS OUTCOMES WITH AI AND ML IN THE ENTERPRISE

Advisory Board (Optum) deploys predictive analytics and machine learning on big data using the container-based platform from HPE’s BlueData team platform. Learn how they streamlined operations and cut costs while enhancing patient care in U.S. hospitals.

Challenge

Helping hospitals across the US translate their big data into actionable insights that deliver business value.

Solution

Deployment of distributed ML and analytics applications and for the separation of compute and memory from storage.

“BlueData has helped us to address these challenges with their containerised solution that has delivered faster time-to-insights, reduced our costs and freed up our staff to innovate. It’s paying big dividends for our organisation, and we look forward to continuing our journey together.”

Ramesh Thyagarajan, Executive Director, Advisory Board (Optum)

Result

Data-driven insights needed to improve operational efficiency, reduce infrastructure costs and enhance patient care.

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Operationalising Machine Learning

This webinar with experts from HPE and Forrester Research looks at the key technological and organisational challenges that impact the success of machine learning (ML) projects.

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1,2,3 Forrester: Operationalise Machine Learning, June 2020