Building Resilient Applications Using Data-Centric AI
September 8, 2022 | 10:00 AM - 10:30 AM Pacific Time
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One of the biggest challenges facing enterprise AI teams is keeping an AI application relevant in the face of ever-changing data and evolving business objectives—the solution to this challenge can be found in data-centric AI development.
Typically data science teams have relied on either rule-based systems or model-centric ML development—each with its own tradeoffs—to build AI applications. Data-centric AI makes it possible to unlock a faster and more resilient way to build AI applications that are more accurate and adaptable in a fraction of the time typically needed for these traditional approaches.
And Fortune 500 data science teams have achieved impressive results by shifting to data-centric AI. For example, a Fortune 50 online bank built a high cardinality utterance classifier and trained the classifier with over 30,000 programmatically labeled conversations in a matter of weeks with a data-centric approach, and adding new intents takes only hours, not days of work.
- Why AI applications built using rule-based systems or model-centric machine learning development workflows are not resilient to changing data for business objectives typically used to build AI applications
- How data-centric AI allows data science teams to build applications that can easily adapt to changing problems or complexity.
- How a Fortune 50 online bank built a more resilient chatbot application that could adapt to changing sentiments with data-centric AI development.
Presented by
Arjun Prakash
Director of Solutions Strategy
Snorkel AI
About the presenter
Arjun Prakash
Arjun heads Snorkel AI’s product and GTM investments for use cases and solutions across industries, including healthcare. Arjun joined Snorkel AI from Palantir, where he was an early employee of the commercial business and spent 8 years building and leading various teams, including healthcare, financial services, and commercial strategy. Prior to Palantir, Arjun was a researcher at BlackRock and Siemens Corporate Research and has a degree in electrical and computer engineering from Cornell University.
