Data Brew by Databricks Enterprise AI: Research to Product | Data Brew | Episode 43
Apr 10, 2025
Dipendra Kumar, a Staff Research Scientist at Databricks, focuses on AI application in enterprises, while Alnur Ali, a Staff Software Engineer, tackles the engineering challenges of deploying AI. They dive into the struggles of messy data, security, and scalability in enterprise AI. The duo discusses how QuickFix improves coding assistance through user feedback. They emphasize the collaboration between research and engineering and explore how generative AI is reshaping programming, highlighting the need for human oversight to enhance productivity.
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Episode notes
Messy Enterprise Data
- Enterprise data is messier than academic data, with missing values and typos.
- Enterprise queries are more complex, with nuances not easily described in academic settings.
High Accuracy Expectations
- Enterprise AI users have higher accuracy expectations than academic users.
- Driverless cars exemplify the need for near-perfect accuracy in real-world applications.
Start Simple
- Start with simple academic approaches when building enterprise AI solutions.
- Address real-world considerations that academia often ignores.