Deep explanation
Implement MLflow correctly in a real-time analytics copilot
Senior implementation question: add MLflow Tracing, prompt and model versioning, registry aliases, mlflow.genai.evaluate release gates, and production feedback to a latency-sensitive analytics copilot in two weeks.
Quick answer
I'd add MLflow Tracing with autologging for the LLM client plus manual spans for query generation and execution, exporting asynchronously so the user request never waits on the tracking server. Prompts and agent config are versioned, the agent is logged as a model, and production loads it through a registry alias like @champion. Every release runs mlflow.genai.evaluate on a golden question set, and only a CI job can move the alias after the scores pass.
The interview question
You have two weeks to add MLflow to a real-time analytics copilot (an LLM agent that turns user questions into metric queries and explains the results). The team wants tracing of every request, versioned prompts and agent configs, evaluation before each release, and a safe way to promote new versions, without adding latency to the user path. What would you implement, with which MLflow features, and how would you validate it?
Deep explanation
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