Thanks to Rohit Israni for contributing this guest blog, originally posted to the Intel AI Builders blog.
Maintaining data science programs in-house can be challenging. In order to make data science a competitive advantage, companies need to be able to rapidly develop, test and deliver high-impact models of their data and manage those models at scale. Additionally, large-scale deployments of analytics solutions require massive amounts of compute, and can place additional strain on infrastructure resources. To be successful with their data science programs, businesses need to be able to easily scale hardware, manage software environments, track work, monitor resources, and automate model deployment and monitoring.
Intel® AI Builders partner Domino Data Lab brings a fresh approach to model management with its data science platform. Based on predictive analytics and artificial intelligence, the platform has practical applications in a wide variety of industries, including financial services, manufacturing, retail, and health care. The platform allows Domino’s customers to benefit from the Intel® Distribution for Python.
The company’s demo at the Artificial Intelligence Conference in San Francisco from September 4-7, 2018 focuses specifically on accelerating the entire data science lifecycle, from exploratory analytics to production model management. It will show how to scale hardware, manage software environments, track work, monitor resources, and automate model deployment and monitoring.
The Artificial Intelligence Conference is co-presented by O’Reilly and Intel. For the full conference schedule and additional details, click here.
Can’t make it to the live demo? Learn how a model is developed and delivered in Domino.