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.
38:03
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Quick takeaways
- Enterprises require AI solutions that achieve near-perfect accuracy to effectively handle messy, real-world data compared to academic models.
- The evolving role of developers now emphasizes strategic thinking, as AI tools automate routine coding tasks, enhancing overall productivity.
Deep dives
Differences Between Enterprise AI and Academic Use Cases
Enterprise use cases for AI demand significantly higher accuracy than those typically seen in academia. For instance, while achieving 80% accuracy with a large language model might be viewed as impressive in an academic context, enterprises often require accuracy closer to 99.999% to meet user expectations. Additionally, enterprise environments are characterized by messy, nuanced data that can contain typos, missing values, or complex queries, unlike the more controlled scenarios in academic research. This emphasizes the need for AI solutions in enterprises to be designed with a focus on high precision and adaptability to real-world complexities.
