Operationalizing Machine Learning: Lessons from the FieldData Science and Advanced Analytics
The rules of business are being rewritten because of abundant data and compute power, and machine learning research and incubation projects are everywhere. But how about the innovation unlocked once you bring machine learning out of research and into production? Learn how to build and operationalize machine learning systems, at scale. This two-part session will also take a deep dive into the newly announced IBM Cloud Private for Data, which helps you prepare your data for AI, leveraging cloud agility, lightning speed and machine learning everywhere. Attendees will receive a free copy of the book "Machine Learning for Dummies."
- Core Curriculum Data and Analytics Data Science and Advanced Analytics
- Cloud and Data Campus Theater A
- Thu (March 22), 11:30AM PT
Dinesh NirmalVice President, Analytics Development IBM Hybrid Cloud, IBM
Dinesh Nirmal's development mission is to build analytics products that allow business to operationalize machine learning for immediate value. He launched 5 international IBM Machine Learning Hubs, where data professionals from industries including finance, manufacturing, and healthcare work with IBM machine learning experts to build prototypes to solve data problems. Major releases during his 2017 tenure as VP, IBM Analytics Development include IBM Integrated Analytics System and IBM's Data Science Experience, which placed IBM as the leader on the 2017 Gartner Magic Quadrant for Data Science. Dinesh focuses on integrating open source software throughout the IBM Analytics portfolio, including Apache Spark, Apache Hadoop, Apache Atlas, and R. He serves on the Board of the R Consortium and led the IBM Spark Technology Center in 2015.Twitter LinkedIn