Operationalise Machine Learning
- Analyst Report
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The emerging field of ML Ops aims to deliver agility and speed to the ML lifecycle – similar to what DevOps has done for the software development lifecycle. A recent Forrester study, commissioned by HPE and Intel, highlights how 98% of enterprise organisations investing in ML Ops believe it will give them a competitive edge. Participants in this study expect that investments will lead to 53% expect increased profitability and 49% anticipate a better adoption of data science best practices and increased skills.
A Forrester study, commissioned by HPE and Intel, shows how 97% of enterprise organisations investing in ML Ops believe it will give them a competitive edge, lead to 53% increased profitability and 49% better adoption of data science best practices.
Brought to you by HPE and Intel®.
HPE Ezmeral ML Ops
HPE provides an enterprise grade container-based platform – HPE Ezmeral ML Ops. HPE Ezmeral ML Ops supports every stage of ML lifecycle – data preparation, model build, model training, model deployment, collaboration and monitoring. HPE Ezmeral ML Ops is an end-to-end data science solution with the flexibility to run on-premises, in multiple public clouds or in a hybrid model and respond to dynamic business requirements in a variety of use cases.
To deliver business insights using real-time analytics, enterprises need an end-to-end strategy that optimises every stage of the data life cycle, from ingestion to archiving, and across the architecture from edge to cloud. Intel’s broad portfolio of technologies, featured in a comprehensive and highly integrated ecosystem of solutions, accelerates data-powered insights. Solutions based on Intel® technology deliver the performance it takes to handle huge data in-memory, plus the flexibility to scale seamlessly up and out on infrastructure you already know and trust.