Training vs Inference Optimization Trade-offs Questions
Covers the trade-offs between training and inference phases in machine learning systems, including strategies to optimize for both sides. Topics include training efficiency (data utilization, convergence, hyperparameter tuning), inference performance (latency, throughput, memory footprint), deployment considerations (model compression, quantization, pruning, distillation), hardware acceleration, serving architectures (online vs batch), update and versioning strategies, and cost-performance modeling in production ML pipelines.
MediumSystem Design
0 practiced
Design a monitoring system to detect model drift that affects inference quality in production. Specify what to log (features, predictions, ground-truth labels), aggregation/metrics to compute (data-distribution shifts, label-lag, calibration drift), alerting thresholds, and how you would tie alerts to retraining pipelines.
MediumTechnical
0 practiced
Discuss the trade-offs of serving predictions from a single larger model versus an ensemble of smaller models. Cover accuracy, latency, cost per prediction, maintenance complexity, and strategies to get ensemble accuracy benefits without incurring full ensemble latency (e.g., cascading, gating, stacking).
EasyTechnical
0 practiced
When choosing between fp16 (float16) and int8 inference for deployment, what are the accuracy, latency, and hardware-support trade-offs to consider? Discuss when fp16 is a better fit (e.g., GPUs with Tensor Cores) versus when int8 wins (e.g., CPUs/accelerators with 8-bit kernels), and list practical validation steps to compare them for your model.
EasySystem Design
0 practiced
Compare online (real-time) and batch (offline) serving architectures for model inference. For each, list typical latency/throughput characteristics, resource provisioning strategies, freshness guarantees, and example use-cases (e.g., fraud detection vs nightly analytics). Describe a hybrid architecture and when to use it.
HardSystem Design
0 practiced
Design an end-to-end real-time ML pipeline that meets strict SLOs for personalization (e.g., P95 inference latency < 150ms) including: event ingestion, streaming feature computation, online feature store, model serving, caching, and cold-start handling. Describe how to ensure feature consistency between training and serving and how to handle high-cardinality features at low latency.
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