AI Engineer: MLOps and Model Deployment Flashcards
6 cards from real AI practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 AI Engineer: MLOps and Model Deployment flashcards as text
What is the role of an A/B test in an MLOps deployment workflow?
Answer: To statistically compare two model versions using live traffic
A/B testing splits live traffic between two model versions and uses statistical analysis to determine which performs better.
Which metric is most useful for detecting concept drift in a classification model?
Answer: Prediction distribution shift over time
Monitoring the distribution of model predictions over time reveals concept drift because the model's output distribution changes as real-world patterns evolve.
What is 'shadow mode' deployment in MLOps?
Answer: Sending production traffic to a new model without using its outputs to serve users
Shadow mode runs a new model on real traffic in parallel with the live model, comparing outputs without impacting users.
Which tool is commonly used for experiment tracking in ML, allowing teams to log parameters, metrics, and artifacts?
Answer: MLflow
MLflow is an open-source platform for tracking experiments, packaging code, and managing model lifecycle.
What is 'online serving' in ML deployment as opposed to 'batch inference'?
Answer: Generating predictions in real time for individual requests
Online serving generates low-latency predictions on demand for individual requests, unlike batch inference which processes large datasets periodically.
Which practice helps prevent training-serving skew in production ML systems?
Answer: Sharing the same feature pipeline code between training and serving
Sharing the same feature transformation code between training and serving ensures the model sees identical feature representations in both contexts.