Prepped for ML design stuff like model selection, training strategies, feature engineering.
Start by clarifying requirements and constraints (e.g., latency, throughput, model size, hardware). Then walk through the pipeline stages: data ingestion, preprocessing, model inference, post-processing, and monitoring. Emphasize trade-offs and justify design choices based on NURO's likely use case (e.g., real-time inference for autonomous systems).
Pro tip: Quantify trade-offs with concrete numbers (e.g., 'batching reduces latency but increases throughput; we can batch up to 32 requests to stay under 100ms'). This shows practical experience and helps interviewers assess your decision-making.
Ask about latency, throughput, model size, hardware constraints, and data characteristics. This ensures the design meets actual needs.
Outline the end-to-end flow: data ingestion, preprocessing, model inference, post-processing, and output. Mention components like load balancers, message queues, and model servers.
Discuss model optimization (quantization, pruning), serving frameworks (TensorFlow Serving, TorchServe), and batching strategies. Address latency vs. throughput trade-offs.
Explain how to scale horizontally (auto-scaling groups, Kubernetes) and ensure fault tolerance (redundancy, retries, circuit breakers).
Cover logging, metrics (latency, error rates), alerting, and model drift detection. Discuss A/B testing and canary deployments for updates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.