Hugging Face Machine Learning Engineer Interview Questions

I would use offline metrics, regression tests, edge-case examples, and if possible a limited rollout or A/B test. Testing should cover both model quality and system behavior because the application can fail even when the model is technically fine. A strong release process is conservative and evidence-based.

I would start with prompting because it is usually faster and cheaper to iterate. If the model still does not behave consistently enough, or if I need a very specific style or task format, then fine-tuning becomes more attractive. The decision depends on how much control I need and how stable the task is.

I would compare the training setup, data pipeline, prompts, and runtime configuration before and after the release. Then I would reproduce the issue on a fixed test set and look for data drift, retrieval changes, or code regressions. Debugging ML systems means treating the model and the surrounding product as one stack.

I would track task quality, drift, latency, error rate, throughput, and user feedback. For generative systems, I would also track refusal behavior, hallucination rate, and cost per request. The exact metrics depend on the product, but they should reflect both model behavior and system health.

I would first measure the imbalance and decide whether the minority class matters enough to require special handling. Then I might use resampling, class weights, threshold tuning, or better feature design. The key is to optimize for the business cost of mistakes, not just raw accuracy.