The original title "AI Data Lake and Lakehouse" is quite vague and generic. I need to make it more specific and punchy based on the summary.

Short summary

The article argues that most 'AI-powered' data lakes are just automation with better branding, and proposes an AI-native lakehouse where ML drives compaction, maintenance, query routing, and governance through continuous learning. It introduces LakeOps, an autonomous control plane for Apache Iceberg that connects to existing catalogs, engines, and storage without data movement or code changes. The system exposes an MCP server so AI agents can query the lake directly, and improves with every query executed.

• •Distinguishes AI-native lakehouses from AI-washed automation
• •Introduces LakeOps as an AI control plane for Apache Iceberg
• •Covers compaction, query routing, governance, and agent enablement

Generated with AI, which can make mistakes.