drift
Applied AI interview questions tagged drift, across every topic.
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Concepts behind "drift"
The curriculum that explains the ideas these questions test.
Foundational
Model Monitoring in ProductionMonitoring an ML model means more than uptime and latency, because a model can be healthy and silently wrong. You monitor four layers: operational (latency, errors, cost), data/input (schema, missing values, drift), prediction (output distribution, confidence), and model quality (accuracy and business metrics, once labels arrive, which lag). Inputs and predictions are leading indicators; labels confirm later. Applied-AI interviews probe it because silent model decay is invisible to ordinary service monitoring.🔁 MLOps & Lifecycle
Core
The Computer Vision PipelineA production CV system is a chain: ingest and version images, preprocess and augment, fine-tune a pretrained backbone, attach a task head, evaluate with sliced metrics, post-process, then serve and monitor. The invariant that separates working systems from broken ones is train/serve consistency: the exact resize, color space, and normalization must match at training and inference. Applied AI interviews probe this because most CV failures live at the preprocessing seam, not in the architecture.📊 Evaluation & ML FoundationsSign in
Core
User Feedback Loops and the Data FlywheelA data flywheel captures implicit and explicit user feedback in production, routes it into eval sets and fine-tuning data, and uses the improved model to attract more usage that generates more feedback. The hard part is not the loop but the signal quality: implicit signals are biased and explicit ratings are sparse and gameable, so naive feedback ingestion teaches the model the wrong thing. Applied AI interviews probe it because a candidate who treats every thumbs-down as ground truth will build a system that degrades while looking like it is learning.⚙️ System Design for AI in ProductionSign in
Core
Drift DetectionModels decay because the world changes. Data drift is a shift in the input distribution (detectable without labels by comparing live features to a training reference with PSI or KS tests); concept drift is a change in the input-to-output relationship (usually needs labels, which often lag). The discipline is monitoring inputs and predictions as leading indicators, alerting on sustained shifts, and triggering retraining. Applied-AI interviews probe it because 'the model was great at launch and quietly got worse' is a top production failure.🔁 MLOps & LifecycleSign in