Your question is Reproducible and Scalable Deployments. 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).
How do you ensure the reproducibility and scalability of the machine learning models you deploy?
Explain a practical approach covering data and code versioning, deterministic training, dependency management, validation, model packaging, deployment, monitoring, and retraining. Include a small implementation outline or code example showing how you would create a repeatable training artifact and prepare it for scalable inference. Discuss how you would detect training-serving skew, data drift, and reproducibility failures.
