MLOps.community

Conversation with the MLflow Maintainers

70 snips
Jan 16, 2026
Corey Zumar, a Product Manager at Databricks and MLflow maintainer, joins Jules Damji, a Lead Developer Advocate and expert on Spark, and Danny Chiao, an Engineering Leader focused on AI governance. They discuss how MLflow is evolving to support generative AI and tool-calling agents. Key topics include challenges in multi-turn conversations, the importance of feedback for optimization, and the need for unified platforms to streamline AI workflows. They also delve into data governance strategies and the balance of cost and quality in model management.
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INSIGHT

Chat Interfaces Are Still Central

  • Chat interfaces remain the dominant AI application, now frequently multimodal and tool-calling under the hood.
  • Developers combine retrieval, API calls, and writes to build richer, action-capable agents.
INSIGHT

Multi-Turn Evaluation Is Essential

  • Observability and multi-turn evaluation for chatbots remain nascent despite production use.
  • Multi-turn judges reveal issues like repetition and logical inconsistency that single-turn checks miss.
ANECDOTE

Launch Without Evals Backfires

  • A customer launched a chatbot without robust evals and saw chats devolve into users swearing at it.
  • That reality exposed the need for better monitoring and targeted fixes.
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