Operationalize Machine Learning
- Analyst Report
- PDF 503 KB
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 organizations investing in ML Ops believe it will give them a competitive edge. Participants of 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.
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 optimizes 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.