cold start

Recsys underpins half of applied ML, and this question checks whether you fit the approach to data availability and hold a real answer for the cold-start problem that breaks naive systems. Here is the foundations answer.

Anomaly detection seems simple until seasonality, cold start, and alert fatigue arrive. The signal is modeling the expected baseline (including weekly and daily cycles), picking unsupervised methods when labels are scarce, and tuning so you do not bury users in false alarms. Here is the design.

New users and new items carry zero interaction history, so collaborative filtering has nothing to work with. The candidates who pass treat it as a lifecycle, not a single trick. Here is how.

Recommending events (or jobs) breaks ordinary recsys in two spots: items expire and items are local. What matters is how you handle perishability, geography, and the fact that every item is a cold-start item. Here is the design.

Every AI product that runs generated code (notebooks, agents, code interpreters) ends up building this, and the interview turns on two numbers: how strong your isolation is and how fast a session starts. The answer is microVMs plus snapshot restore, and knowing why the VM boot is not the slow part.

Autoscaling GPUs is nothing like autoscaling web servers: GPUs are scarce and expensive, model loading is slow, and the right signal is not CPU. The signal is scaling on queue and GPU metrics, taming cold starts, and the scale-to-zero economics.

A newly booted replica is sluggish on its opening requests while weights load and kernels compile, which fights with reactive autoscaling. The signal is identifying where cold start comes from plus the warmup and provisioning remedies.

A track runs three minutes, a session runs an hour, and a fresh release has zero plays on launch day. The interview centers on mixing collaborative filtering with audio content embeddings, handling cold start on both sides, and reading a skip as the loud negative it really is.

This model's output is a price input, not a ranking. A miscalibrated CTR skews the auction bid, so you overpay or underdeliver. Throw in conversions that arrive days after the click and advertisers with no history, and calibration plus delayed feedback become the entire interview.

New courses launch every week and most browsers are signed-out or brand new, so the interesting half of this problem is what you recommend when you have almost no behavioral signal. The other half is avoiding the trap of optimizing engagement into a clickbait catalog.