Bridging the Gap Between AI and Business Data // Deepti Srivastava // #325

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Bridging the Gap Between AI and Business Data // MLOps Podcast #325 with Deepti Srivastava, Founder and CEO at Snow Leopard.

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// Abstract

I’m sure the MLOps community is probably aware – it's tough to make AI work in enterprises for many reasons, from data silos, data privacy and security concerns, to going from POCs to production applications. But one of the biggest challenges facing businesses today, that I particularly care about, is how to unlock the true potential of AI by leveraging a company’s operational business data. At Snow Leopard, we aim to bridge the gap between AI systems and critical business data that is locked away in databases, data warehouses, and other API-based systems, so enterprises can use live business data from any data source – whether it's database, warehouse, or APIs – in real time and on demand, natively. In this interview, I'd like to cover Snow Leopard’s intelligent data retrieval approach that can leverage business data directly and on-demand to make AI work.

// Bio

Deepti is the founder and CEO of Snow Leopard AI, a platform that helps teams build AI apps using their live business data, on-demand. She has nearly 2 decades of experience in data platforms and infrastructure.

As Head of Product at Observable, Deepti led the 0→1 product and GTM strategy in the crowded data analytics market. Before that, Deepti was the founding PM for Google Spanner, growing it to thousands of internal customers (Ads, PlayStore, Gmail, etc.), before launching it externally as a seminal cloud database service. Deepti started her career as a distributed systems engineer in the RAC database kernel at Oracle.

// Related Links

Website: https://www.snowleopard.ai/

AI SQL Data Analyst // Donné Stevenson - https://youtu.be/hwgoNmyCGhQ

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Timestamps:

[00:00] Deepti's preferred coffee

[00:49] MLflow vs Kubeflow Debate

[04:58] GenAI Data Integration Challenges

[09:02] GenAI Sidecar Spicy Takes

[14:07] Troubleshooting LLM Hallucinations

[19:03] AI Overengineering and Hype

[25:06] Self-Serve Analytics Governance

[33:29] Dashboards vs Data Quality

[37:06] Agent Database Context Control

[43:00] LLM as Orchestrator

[47:34] Tool Call Ownership Clarification

[51:45] MCP Server Challenges

[56:52] Wrap up