As artificial intelligence and machine learning capabilities and possibilities continue to evolve within enterprise IT infrastructures and business plans, ParallelM is moving to make the process simpler and faster for business users.
That's the idea of its new machine learning automation application, MLOps, which aims to help enterprises bring in and scale up AI and machine learning within their core operations by reducing some of their existing design, deployment and configurational complications.
MLOps provides visualization and collaborative tools for data scientists and operations teams within enterprises so they can work together to bring machine capabilities to their companies, while also ensuring machine learning modeling and prediction quality, according to ParallelM. MLOps also allows business teams to mitigate risks and ensure compliance, while also assessing and optimizing the ROI on their AI initiatives through a unified machine learning application that covers a company's full machine learning production lifecycle.
MLOps is powered by ParallelM's Intelligence Overlay Network technology, which manages the relationship between the machine learning pipelines, events, predictions, policies and dataflows created by users, while minimizing direct interactions with the complex infrastructure behind the scenes.
MLOps can be deployed in the 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.
The idea for MLOps came from discussions with enterprise leaders who were looking for new ways to solve some of the difficult issues of integrating machine learning and AI with business practices and procedures, Sivan Metzger, the CEO of ParallelM, told ITPro Today.
"When a business wants to solve a problem using machine learning, they hire computer scientists and data scientists to create and put these machine learning models into operation and production," said Metzger. "What [MLOps] really helps … is it that it [usually] takes about a two- to three-year period for companies to be ready for a machine learning environment by building the relevant machine learning algorithms and models. Then they need to scale from two to three to 100 or more" to get their operations moving efficiently.
"You can run two or three under somebody's desk, but when you want to run 100 or more that's when you need an MLOps solution," he said. "Practically all the banks and health care organizations we are talking with are using a few machine learning processes now but need help to accelerate to more."
MLOps is filling that gap, he said, by making the processes easier and drastically shortening the timeline to get machine learning operations deeper into enterprises today.
"Companies are doing machine learning, but when you want to scale them into production, that's where the rubber hits the road," said Metzger. "The hard part is putting it into production," which is where companies have little experience.
"This gap I'm describing really requires a bridge and this bridge is MLOps," he said.
So far, the company has three enterprise customers using their product, including a large North America-based bank, a large healthcare service provider and a technology company in the digital advertising marketplace, according to Metzger.