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.
EasyTechnical
75 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.
MediumTechnical
105 practiced
Explain ZeRO-style optimizer state/model sharding (e.g., ZeRO stages 1-3). Describe the trade-offs between memory saving, communication overhead, complexity of implementation, and when you would pick each ZeRO stage for very large transformer training.
HardSystem Design
106 practiced
You run large-scale training on cloud spot instances to reduce cost. Design a fault-tolerant distributed training pipeline that minimizes lost work on preemption. Include checkpointing frequency, elastic worker handling, consistent shuffling of data, and how to resume a job across different instance types.
MediumTechnical
99 practiced
Design an A/B/canary rollout strategy for iterative model updates to minimize inference regressions. Specify traffic splitting, statistical testing approach, rollback criteria, how to run shadow tests, and what telemetry you would collect to decide when to promote a candidate model to 100%.
HardTechnical
89 practiced
As the lead AI Engineer, you must decide how to allocate a small engineering team's time between improving training efficiency (faster iteration and lower cloud cost) and reducing inference latency (better user experience) for a product with 1M daily active users. Describe a framework to quantify and prioritize work items, stakeholder communication you would perform, and short-term vs long-term investments you'd recommend.
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