Demonstration and Project Close

Step 1: Prepare the Demonstration

Run through the full system before sending anything to Emeka. Start the API and verify each piece of infrastructure:

• Call /health and confirm the model is reported as loaded with the correct version
• Send a valid prediction request and confirm the response includes the prediction, probability, and model version
• Send an invalid request and confirm you get a structured error response
• Check the MLflow UI and confirm the experiment runs are visible with their parameters and metrics

This is a system-level verification. Each piece worked individually when you built it. Now confirm they work together.

Step 2: Send Emeka a Summary

Write Emeka a summary of what was built, translating the infrastructure into his terms. He doesn't know what Pydantic is. He doesn't care about MLflow's API. What he cares about: does the system tell him when it's down? Can his team experiment safely? Can someone else reproduce it?

Something like: "The API now checks its own health and reports when it can't serve predictions. It validates input so bad requests get clear error messages instead of crashes. Every prediction includes the model version so you can trace any result back to the model that produced it. Your data team can run experiments -- each run is tracked with its settings and results, and everything is reproducible."

Step 3: Receive Emeka's Assessment

Emeka will check against his three original concerns. He'll ask: "So if the API goes down tomorrow, the health check will flag it before anyone tries to use it?" Confirm.

He'll follow up about his data team: "They can run experiments now without breaking the live predictions?" Explain how the experiment tracking setup works.

If the infrastructure is solid, Emeka is satisfied: "This is what I needed. The system isn't just running anymore -- it's reliable."

He'll ask one more question: "If you left tomorrow and someone else had to take over -- could they reproduce what you built?" This is the reproducibility check. With pinned versions, random seeds, and documented experiment tracking, the answer should be yes.

Step 4: Write the Project README

Direct Claude to write a README covering: what was built (infrastructure for the churn prediction API), the components (input validation, health checks, model versioning, structured errors, experiment tracking, reproducibility), how to run the system, and how to use the experiment tracking.

Review the README for completeness and accuracy.

Step 5: Final Commit

Verify all project files are committed. Direct Claude to check the repo status, stage any uncommitted files, and push to GitHub with a clear commit message.

The repository should contain: API code with input validation, health check endpoint, model versioning in responses, structured error handling, MLflow experiment configuration, pinned requirements.txt, and README.