Enterprise AI service provider DataRobot has unveiled MLOps, a machine learning operations (MLOps) solution for deploying, monitoring, and managing machine learning models across the enterprise. MLOps combines DataRobot's existing model management and monitoring solution with capabilities from MLOps category leader ParallelM, which DataRobot acquired in June. DataRobot's new MLOps offering provides a centralised hub for deployment, monitoring, and governance of models created from a variety of tools. As a result, organisations will be able to cut the time it takes them to deploy and scale machine learning-based services in production. Despite the investments in data science teams and infrastructure, many companies have not been able to derive measurable value from AI projects.

Your organization may look like it is well on the way to a machine learning future. Your team is beyond the basics. They are now creating machine learning models that could impact real business problems. Yet they can only do that if those models are implemented. Once those models are created, many organizations seem to be experiencing a disconnect in getting them implemented.

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. 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."

ParallelM, one of the fastest growing new companies in machine learning management, today announced ParallelM MLOps, the first software solution for operationalizing machine learning (ML) and deep learning across the enterprise. Operationalizing machine learning poses a significant challenge, as current techniques have limited ability to tackle the unique intricacies of ML behavior patterns. Prevailing workarounds tend to be manual and brittle, inhibiting ML service scaling, and delaying the benefits of ML to the business. According to a recent survey of over 3,000 "AI-aware" C-level executives by McKinsey Global Institute, only 20 percent have deployed at least one AI technology and only 10 percent have deployed three or more. Further, out of 160 AI use cases examined, only 12 percent had progressed beyond the experimental stage.