## Problem You are given historical telemetry from a distributed service: `(timestamp, qps, p50_latency_ms, p99_latency_ms, error_rate, cpu_util)`. Build a model to predict `p99_latency_ms` and `error_rate` given a future `qps` and `cpu_util`. Walk through: 1. **Feature engineering** — what features to derive from raw telemetry 2. **Model selection** — linear regression, gradient boosting, or neural network; justify your choice 3. **Evaluation** — what metrics matter for ops use cases (MAPE, RMSE, quantile loss?) 4. **Serving** — how the model is used in a capacity planning workflow ## Example Scenario ``` Historical data shows: qps=500 -> p99=20ms, error_rate=0.1% qps=800 -> p99=45ms, error_rate=0.5% qps=1000 -> p99=200ms, error_rate=5% # near saturation Question: predict p99 and error_rate at qps=900 given cpu_util=75% ``` ## Follow-ups 1. How do you handle concept drift when the underlying system changes (e.g., new hardware)? 2. The latency vs. QPS relationship is nonlinear near saturation — how does your model capture that? 3. How would you quantify uncertainty in your predictions for risk-aware capacity planning?

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Performance Modeling: Predict System Latency Under Variable Load Using ML

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Problem You are given historical telemetry from a distributed service: (timestamp, qps, p50_latency_ms, p99_latency_ms, error_rate, cpu_util). Build a model to predict p99_latency_ms and error_rate given a future qps and cpu_util. Walk through: Feature engineering — what features to derive from raw telemetry Model selection — linear regression, gradient boosting, or neural network; justify your choice Evaluation — what metrics matter for ops use cases (MAPE, RMSE, quantile loss?) Serving — how t…

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This is a candidate experience report from a anthropic interview during the onsite round.

It covers the following topics: System Design, Other, Mle, Onsite .

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Performance Modeling: Predict System Latency Under Variable Load Using ML

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