Product data and information architecture experts, Chantal Schweizer and Dave Skrobela, from Earley Information Science, walk through the elements of successful AI programs, the role of data quality and data architecture, and the best way to prioritize AI applications.
It’s All About the Data Guest: Kirk Marple
The Holy Grail of AI Guest: Alex Babin
Recently I chaired the Artificial Intelligence Accelerator Institute Conference in San Jose – in the heart of Silicon Valley. The event has brought together industry innovators from both large and small organizations, providing a wide range of perspectives. For example, the CEO of AI and ML testing startup of Kolena, Mohamed Elgendy and Srujana Kaddevarmuth, Senior Director, Data & ML Engineering, Customer Products, Walmart Global Tech discussed productization of AI solutions and ways to increase adoption. I especially liked the idea of a model catalogue from which data scientists can retrieve data sets and machine learning models that others have built rather than starting from scratch.
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