In this Intel on AI podcast episode: Organizations want to get insights from their data but face barriers to adopting machine learning (ML) and AI including lack of data science expertise in the global workforce, exorbitant costs, lack of guidance, and time commitments of traditional modeling methods. Ben Taylor, the Chief AI Evangelist at DataRobot, stops by the Intel on AI podcast to discuss how the DataRobot enterprise AI platform enables organizations build and deploy accurate ML models in a fraction of the time needed in comparison with traditional data science methods. He describes his work helping organizations overcome many of the obstacles they face when implementing AI in their business models including identifying the important business problem to solve for an organization rather than the most interesting problem. Ben also talks about a challenge DataRobot recently faced having a limitation on the size of data sets that they could train due to limited memory availability on their platform. DataRobot worked with the Intel AI Builders program to optimize their platform to utilize Intel DC Optane Persistent Memory which enabled DataRobot to provide customers with the ability to train incredibly large data sets up to 100+ gigabytes. This gives businesses the ability to truly unlock the potential of all of their data and not be hindered by smaller training data sets. Ben also talks about DataRobot is working hard to help organizations implement AI in an ethical way and protect against bias in AI algorithms.

To learn more, visit https://www.datarobot.com/ and join the conversation at:
community.datarobot.com

Visit Intel AI Builders at:
builders.intel.com/ai

Tags: AI, artificial intelligence, DataRobot, deep learning, Intel Optane DC PMM, machine learning, ML, natural language processing, Supercomputing
 
Posted in: Artificial Intelligence, Intel, Intel on AI