SUNNYVALE, Calif., Feb. 21, 2018 /PRNewswire/ -- 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.
MLOps delivers a unique approach, addressing ML production issues head-on, by automating ML-optimized continuous deployment and integration, ensuring ML model and prediction quality, and empowering data science and operations teams with innovative visualization and collaboration facilities. Using MLOps, business teams can mitigate risk, ensure compliance, assess, and optimize the ROI of their AI initiatives. By providing a single, unified software solution for the full ML production lifecycle, MLOps enables enterprises to move confidently into the critical phase of realizing ML business value.
"Rapid and robust deployment of ML-driven business services across the enterprise requires bringing together technology and process optimized to handle the unique complexities of ML pipelines," said Sivan Metzger, CEO of ParallelM. "Our solution helps manage the whole 'ML production lifecycle' by automating the core elements and functions required for scaling out the live delivery of ML-driven business services. Our customers tell us that 'MLOps is not just a software solution, it's the way we chose to structure our organization, and how we run our business.' We are truly changing the way enterprises are approaching the scaling of machine learning and AI."
MLOps is powered by ParallelM's ION™ (Intelligence Overlay Network) technology, a proprietary, patent-pending approach that provides a comprehensive, logical representation of any ML-driven business application. The ION manages the relationship between the ML pipelines, events, predictions, policies and dataflows, while abstracting away the underlying composite of model dependencies and compute infrastructure. This enables operations teams to organize their efforts around managing ML business services rather than individual components within the ML stack.
MLOps can be deployed on cloud, on premise, or in hybrid scenarios and works across distributed computing platforms such as Apache Spark™, TensorFlow™, Apache Flink®, and PyTorch. MLOps also integrates with leading data science and AI developer platforms.
ParallelM will be showcasing its MLOps software solution in Booth 1426 at the Strata Data Conference, March 6-8, in San Jose, CA. To make an appointment to meet with ParallelM at the conference, contact jmoritz@0to5.com.
To learn more about ParallelM's MLOps solution, visit www.parallelm.com.
About ParallelM
ParallelM is the first and only company completely focused on delivering machine learning operationalization at scale. ParallelM's breakthrough MLOps™ 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 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 MLOps 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.
Media Contact:
Jennifer Moritz for ParallelM
jmoritz@0to5.com
917-748-4006
SOURCE ParallelM