AML Model Deployment & MLOps Flashcards

What is a feature store in an MLOps architecture?

Answer: A centralized repository for storing, sharing, and serving precomputed features for ML models

Feature stores provide consistent, reusable features across training and serving pipelines, eliminating training-serving skew.

What is the purpose of model versioning in MLOps?

Answer: To track distinct model iterations with metadata to enable rollback and comparison

Model versioning logs each trained model artifact with its configuration, metrics, and data lineage to support governance and rollback.

Which metric category is most critical when monitoring a deployed classification model in production?

Answer: Prediction confidence distribution and accuracy on live data

Monitoring live prediction confidence and accuracy reveals model drift and performance degradation before it significantly impacts business outcomes.

What is shadow deployment in machine learning operations?

Answer: Running a new model in parallel with the production model without using its predictions for actual decisions

Shadow deployment routes live traffic to both the current and new model, logging new model outputs for evaluation without affecting end users.

What is the primary purpose of model explainability tools like SHAP in production ML systems?

Answer: To explain individual predictions and ensure model transparency for stakeholders and auditors

SHAP attributes each feature's contribution to individual predictions, supporting transparency and regulatory compliance in production.

What is canary deployment in ML production systems?

Answer: Gradually rolling out a new model to a small subset of users before a full release

Canary deployment incrementally increases traffic to a new model version, allowing real-world validation with minimal risk exposure.