Covers designing and operating machine learning systems to handle growth in data volume, model complexity, and traffic. Topics include distributed training strategies such as data parallelism, model parallelism, and pipeline parallelism; coordination and orchestration approaches like parameter servers, gradient aggregation, and framework tools such as PyTorch distributed, Horovod, and TensorFlow strategies; data pipeline and I O considerations including sharding, efficient formats, preprocessing bottlenecks, streaming and batch ingestion; serving and inference scaling including model sharding, batching for throughput, autoscaling, request routing, caching, and latency versus throughput tradeoffs. Also includes monitoring, profiling, checkpointing and recovery, reproducibility, cost and resource optimization, and common bottleneck analysis across network, storage, CPU preprocessing, and accelerator utilization.
MediumTechnical
33 practiced
Collective all-reduce operations are saturating the interconnect during multi-node training and slowing the run. Propose optimizations across software and infrastructure: hierarchical all-reduce, overlapping communication with computation, gradient compression/quantization, topology-aware scheduling, NCCL tuning, and hardware upgrades. Discuss trade-offs for each approach.
EasyTechnical
28 practiced
List and justify the most important metrics to monitor for a long-running distributed model training job: GPU utilization, CPU utilization, memory usage, network throughput, examples/sec, training/validation loss curves, gradient norms, checkpoint age, and node health. For each metric, describe what a critical alert might indicate and give an example threshold.
EasyTechnical
33 practiced
Compare common storage formats for ML training data (raw files, CSV, Parquet, TFRecord, RecordIO). For each format discuss sequential read throughput, seek cost, compression support, schema evolution, streaming suitability, and how format choice impacts CPU preprocessing and GPU feeding pipelines.
HardTechnical
30 practiced
Discuss consistency and staleness trade-offs between parameter-server architectures with asynchronous updates and synchronous ring-allreduce training. Address convergence properties under staleness, communication efficiency, fault tolerance, and practical heuristics such as bounded staleness or elastic averaging.
MediumTechnical
31 practiced
Explain how converting a dataset of millions of small JPEG files into sharded binary formats (TFRecord, sharded tar, or Parquet for tabular features) improves training throughput. Discuss trade-offs around ingestion flexibility, incremental updates, random access patterns, host CPU parsing cost, and a migration plan.
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