Your question is ML Deployment Environment Reproducibility. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You're preparing to deploy a Python-based ML model and want the pipeline to behave the same in development, training, and production. You need a clear approach for managing package versions, build artifacts, and runtime consistency.
How do you handle package dependency management and environment reproducibility when deploying Python-based ML models?