September 24, 2018
ParallelM Named a 2018 Gartner Cool Vendor in Data Science and Machine Learning
BY PARALLELM

Leading analyst firm selects cool vendors in data science and machine learning

SUNNYVALE, Calif., September 24, 2018 – ParallelM, one of the fastest-growing companies in machine learning operationalization (MLOps), today announced that it has been named a “Cool Vendor” based on the September 11, 2018 report titled, “Cool Vendors in Data Science and Machine Learning,” by Peter Krensky, Svetlana Sicular, Jim Hare, Erick Brethenoux, and Austin Kronz at Gartner, Inc. Gartner’s report notes, “While the democratization of machine learning platforms is proliferating model creation, the need to operationalize models at scale is still a looming challenge. Vendors focusing on this piece of the machine learning life cycle can answer a growing demand in the market.”

“We believe it is an honor to be named as one of Gartner’s ‘Cool Vendors’ this year in the areas of machine learning and data science,” said Sivan Metzger, CEO of ParallelM. “As the interest in these areas continues to grow at a rapid pace, technology has brought us to a point where ML models are being created and tested at scale. But what happens then? How can companies actually operationalize these models and derive the value out of ML? With our solution, companies are finally able to automate the deployment and scale of their machine learning applications, finally unlocking their true business value.”

Today, implementing machine learning (ML) poses significant challenges for enterprises. Data science development platforms and workbenches have no real understanding of production constraints, while current operations solutions and collaboration methods have a limited ability to tackle the complexities of ML behavior patterns. This is particularly evident while they run in production. The disconnect between development and operations – from both the technology side and the process side – is the key inhibitor preventing ML from being scaled, and delays the desired ROI and its benefits to enterprises.

Through its unified software solution, MCenter, ParallelM delivers a unique approach to addressing the challenges enterprises face while implementing ML and data science across the enterprise: machine learning operationalization (MLOps). MLOps is a practice that enables companies to establish a culture and environment where machine learning technologies can generate business benefits by rapidly, frequently and reliably building, testing and releasing machine learning models on production applications.

For more information on ParallelM or MCenter, visit www.parallelm.com.

Vview or download the 2018 “Cool Vendors in Data Science and Machine Learning” report, here.

Required Disclaimer:
Gartner does not endorse any vendor, product or service depicted in our research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About ParallelM

ParallelM is the first and only company completely focused on delivering machine learning operationalization (MLOps) at scale. ParallelM’s breakthrough MCenter™ solution is built specifically to power the deployment and management of machine learning pipelines in production, so that companies can scale machine learning delivery across their business applications. ParallelM’s approach is that of a single, unified MLOps solution that embeds best practice processes in technology, enabling collaboration across all ML stakeholders to unlock the business value of AI. Please visit www.parallelm.com or email us at info@parallelm.com.

ParallelM and MCenter are trademarks of Parallel Machines, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.

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