real time

Extreme class imbalance plus a hard latency budget plus an adversary who adapts. The signal is handling imbalance honestly, choosing the operating point from costs, and designing for the feedback loop. Here is the end-to-end design.

Ads ranking is where calibrated probabilities meet hard latency and money. The signal is knowing that CTR must be calibrated (not just ranked), the feature and serving design, and the auction context. Here is the design that goes beyond 'train a classifier.'

Dynamic pricing blends demand forecasting, optimization, and real-time serving, with fairness and trust constraints. The signal is the predict-then-optimize structure plus guardrails against perverse outcomes. Here is the design.

The trap is replacing the classifier with an LLM. The real-time, imbalance, and adversarial constraints do not go away. The signal is a hybrid: a fast calibrated model scores inline, LLMs investigate the gray zone off the hot path.

ETA is spatiotemporal regression served in real time over conditions that change by the minute. The signal is feature freshness, segment or stage decomposition, and the asymmetric cost of being wrong. Here is the design.

Spam detection is adversarial, imbalanced classification under a low-latency bar. The interviewer is watching for whether you set the threshold by cost asymmetry and lean on signals spammers cannot fake. Here is the design.

AV perception is safety-critical, real-time, multimodal ML: detect and track everything around the vehicle from multiple sensors. The signal is sensor fusion plus detection/tracking under a hard latency budget and a fail-safe bar. Here is the design.

Cleaning noise out of speech is a tradeoff between how good it sounds and how fast it runs. A strong answer picks spectrogram masking versus waveform models against a real-time budget, names PESQ and STOI as the metrics, and is honest about the artifacts each approach introduces.

A rider taps request and within seconds a nearby driver is assigned. The interesting problems are geospatial indexing of moving drivers, the matching objective (nearest is not always best), and handling the race where two riders want the same car. Here is how to build it.