cold start

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

Anomaly detection sounds easy until seasonality, cold start, and alert fatigue hit. The signal is modeling the expected baseline (including weekly and daily cycles), choosing unsupervised methods for scarce labels, and tuning to avoid drowning users in false alarms. Here is the design.

New users and new items have 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 normal recsys in two places: items expire and items are local. The signal is how you handle perishability, geography, and the fact that every item is a cold-start item. Here is the design.

Autoscaling GPUs is not 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 freshly started replica is slow on its first requests while it loads weights and compiles kernels, and that clashes with reactive autoscaling. The signal is naming the cold-start sources and the warmup and provisioning fixes.