Everything in Data Science & Analytics, page 2
Risks of optimizing recommendations only for CTR
CTR-only invites clickbait, low satisfaction, and long-term churn; add counter-metrics like dwell time, satisfaction, retention, and diversity.
Federated learning architecture, risks, and defenses
Devices train locally and send updates not data, a server aggregates; gradients still leak, enabling inversion or membership inference; defend with secure aggregation and DP.
Differential privacy, epsilon, and noisy aggregates
Define DP as bounded output change when one record is added or removed, explain epsilon as the privacy-accuracy knob, add calibrated noise scaled to sensitivity.
k-anonymity and its limits against linkage attacks
Define k-anonymity via indistinguishable quasi-identifier groups, apply generalization and suppression, then show homogeneity or linkage attacks break it.
Right to be forgotten and machine unlearning
Delete raw data everywhere, then remove influence via full retraining, SISA sharded retraining, or approximate unlearning; note cost and verification.
Quantifying performance disparity across subgroups
Compute per-group precision, recall, FPR, FNR, compare via fairness metrics; visualize with grouped bars or per-group confusion matrices.
Programmatically detect and redact PII in text
Regex for structured PII plus NER for names and places, redact or tokenize, then validate recall.
Minimizing model prediction latency end to end
Shrink the model via quantization, pruning, distillation, compilation; cut infra latency via caching, edge, faster runtimes.
Zero-downtime model updates with blue-green or canary
Blue-green swaps full traffic after validation, canary ramps a small slice; both need health, latency, and quality monitoring plus instant rollback.
Why version data and model artifacts, not just code
Code alone cannot reproduce a model; data and artifact versioning enable rollback, debugging, audit.
CPU versus GPU serving: cost, latency, throughput
GPUs win on throughput for batched parallel work but cost more; CPUs suit low-volume or small models.
ML CI/CD versus traditional software CI/CD
Validates code plus data plus the model, auto-trains and evaluates, adds continuous training and monitoring.
What a feature store solves: skew and consistency
Central repository of computed features, one definition serving training and inference, reuse across models.
Detecting and responding to model and concept drift
Define drift, pick a metric like PSI or falling AUC against labels, then investigate, retrain, validate.
Batch prediction versus online real-time prediction
Batch is scheduled bulk scoring, online is low-latency per-request scoring; contrast latency, freshness, cost; give a use case each.
Deploy a saved model as a REST prediction service
Load the artifact, wrap it in a predict API, containerize, host with autoscaling, add monitoring.
Explain k-means user segments to a marketing team
Name each segment, profile its defining traits, show size and value, recommend an action.
Two ways accurate data can still mislead in a chart
Name distortions like truncated axes or cherry-picked ranges, give the fix for each, explain why each misleads.
Reconcile rising sign-ups with falling revenue per user
Reconcile the metrics via total revenue and segment mix, frame the tradeoff, recommend an action.
Visualizing long-term trend versus seasonality
A line chart over the full three years, often with a moving average, shows the long-term trend; a seasonal plot overlaying each year by month, or a month-of-year box plot, reveals…
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