ParallelM MLOps Accelerates and Streamlines Delivery of Machine Learning Across the Enterprise to Maximize the Business Value of AI
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.
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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. 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,” said Sivan Metzger, CEO of ParallelM.
“The MLOps framework is similar to the theoretical framework we have been discussing for some time, which is the reason we got a clear thumbs up from our chief architect, as well as the development team. I believe that ParallelM will not only be of value to Clearsense, but also to the healthcare industry as a whole. Many of the organizations that have adopted Hadoop and other big data technologies could use ParallelM’s help as they implement and scale their data science programs. ParallelM has done a great job and we are very pleased to become their partner,” said Charles Boicey, Chief Innovation Officer at Clearsense, a global ML service provider for the healthcare industry.
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.
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