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RichardHatheway

HPE Ezmeral ML Ops Recognized by Gartner

HPE Ezmeral ML Ops Listed as a Representative Vendor for ModelOps in 2021 Gartner® Market Guide for AI Trust, Risk, and Security Management

HPE Ezmeral Recognized by Gartner.png

Overview

On September 1, 2021 Gartner published their 2021 “Market Guide for AI Trust, Risk and Security Management”. Per Gartner, “This Market Guide identifies new capabilities that data and analytics leaders must have to ensure model reliability, trustworthiness and security, and presents representative vendors who implement these functions.”1

At HPE, we believe HPE Ezmeral ML Ops was recognized for the advantages our solution provides to our customers. As such, we’re proud to announce that Gartner listed HPE Ezmeral ML Ops as a Representative ModelOps Vendor in the 2021 “Market Guide for AI Trust, Risk and Security Management.”

TRiSM Market Definition

Gartner defines the AI Trust, Risk and Security Management (TRiSM) market as being made up of multiple software segments. Those segments are governance, trustworthiness, fairness, reliability, efficacy, security, and data protection.

Gartner has also identified core capabilities within TRiSM they believe are necessary to manage new risks and threats that are introduced by artificial intelligence (AI). The core capabilities are shown in the graphic below.

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There are also a variety of tools and solutions that include model interpretability, model explainability, AI data protection, model operations (ModelOps), data anomaly detection, and adversarial attack resistance. The use of tools and solutions from these categories helps enterprises implement AI-specific trust, risk, and security management measures.

It should be noted that multiple vendors provide solutions within these pillars. Per Gartner, “AI Trust, Risk and Security Management typically requires organizations to implement a best-of-breed tool portfolio approach, as most AI platforms will not provide all required functionality.”

ModelOps Market Situation

Gartner defines “model operationalization” as being primarily focused on the end-to-end governance and life cycle management of all analytics, AI, and decision models (including analytical models and models based on machine learning, knowledge graphs, rules, optimization, linguistics, agents, and others). In addition, Gartner also states, “As per Gartner’s 2019 AI in Organizations survey, machine learning was the most leveraged AI technique, but not the only one.”1

The reality is that today, more and more businesses are beginning to embrace AI and machine learning (ML). Modern enterprises understand the benefits ML can provide and want to expand its use. They want to use these tools to operationalize the ML lifecycle and derive insights from their data, as the ability to do that provides a key competitive advantage for businesses today. However, as they attempt to operationalize their ML models, businesses are running into last mile problems related to model deployment and management.

HPE Ezmeral ML Ops can help solve those challenges.

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Challenges to Operationalizing Models

Much like pre-DevOps software development, most organizations today lack streamlined processes for their ML workflows, which causes many projects to fail. This in turn inhibits model deployment into current business processes and applications.

And while it may seem like a straightforward solution to use DevOps tools and practices for the ML lifecycle, it’s really not. ML workflows are very iterative in nature and off-the-shelf software development tools and methodologies won’t work.

That’s where HPE Ezmeral ML Ops can help.

The Solution

HPE Ezmeral ML Ops addresses the challenges of operationalizing ML models at enterprise scale by providing DevOps-like speed and agility, an open-source platform that delivers a cloud-like experience, and pre-packaged tools to operationalize the machine learning lifecycle from pilot to production. It supports every stage of the ML lifecycle—from data preparation to model build, model training, model deployment, collaboration, and monitoring. HPE Ezmeral ML Ops also provides enterprises with an end-to-end data science solution with the flexibility to run on-premises, in multiple public clouds, or in a hybrid model and respond to dynamic business requirements in a variety of use cases.

To learn more about HPE Ezmeral ML Ops and how it can help your business, visit hpe.com/info/mlops or contact your local sales rep. 

1 - Gartner Market Guide for AI Trust, Risk and Security Management (AI TRiSM), Avivah Litan, Farhan Choudhary, Jeremy D’Hoinne, September 1, 2021.

If you’re a Gartner client, you can access the full report here.

Disclaimer:

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation.

Richard

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About the Author

RichardHatheway

Richard Hatheway is a technology industry veteran with more than 20 years of experience in multiple industries, including computers, oil and gas, energy, smart grid, cyber security, networking and telecommunications. At Hewlett Packard Enterprise, Richard focuses on GTM activities for HPE Ezmeral Software.