HPE accelerates Artificial Intelligence innovation with enterprise-grade solution for managing entire machine learning lifecycle

HPE ML Ops transforms AI initiatives from experimentation and pilot projects to enterprise-grade operations and production by addressing the entire machine learning lifecycle from data preparation and model building, to training, deployment, monitoring, and collaboration.

“Only operational machine learning models deliver business value,” said Kumar Sreekanti, SVP and CTO, Hybrid IT at HPE. “And with HPE ML Ops, we provide the only enterprise-class solution to operationalize the end-to-end machine learning lifecycle for on-premises and hybrid cloud deployments. We’re bringing DevOps speed and agility to machine learning, delivering faster time-to-value for AI in the enterprise.”

“From retail to banking to manufacturing to healthcare and beyond, virtually all industries are adopting or investigating AI/ML to develop innovative products and services and gain a competitive edge. While most businesses are ramping up on the build and train phase of their AI/ML projects, they are struggling to operationalize the entire ML lifecycle from PoC to pilot to production deployment and monitoring,” said Ritu Jyoti, program vice president, Artificial Intelligence (AI) Strategies at IDC. “HPE is closing this gap by addressing the entire ML lifecycle with its container-based, platform-agnostic offering – to support a range of ML operational requirements, accelerate the overall time to insights, and drive superior business outcomes.”

“Our online games generate billions of data points every day,” says Alex Ryabov, head of Data Services at Wargaming. “Using complex ML models, our data scientists leverage this data for prescriptive analytics to improve our players’ experience, lifetime value, and loyalty. With HPE’s BlueData software, we’re containerizing these ML and analytics environments to help improve operational efficiency and optimize our business.”

With the HPE ML Ops solution, data science teams involved in building and deploying ML models can benefit from the industry’s most comprehensive operationalization and lifecycle management solution for enterprise AI[1]:

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