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.
HardSystem Design
0 practiced
Design a multi-tenant ML training platform for 50 teams that supports fair-share scheduling, GPU quota enforcement, per-tenant dependency isolation, reproducible environments, dataset sharing, security isolation, and observability. Describe the architecture, scheduler, resource manager, storage model, and how you would implement fair-share and billing.
MediumTechnical
0 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.
MediumSystem Design
0 practiced
Design a training data pipeline for 5TB of images that needs to feed 1000 GPUs at 200 samples/sec each (200k samples/sec). Requirements: minimize GPU idling, support distributed preprocessing and augmentation, provide reproducible shuffles, and allow caching hot data. Outline components (storage, sharding, preprocessing, caching, networking), data formats, and expected bottlenecks with mitigations.
HardTechnical
0 practiced
Compare GPUs and TPUs for large-scale training and inference. Discuss differences in raw compute characteristics (FLOPS, matrix multiply throughput), memory architecture and bandwidth, programmability and ecosystem maturity, quantization/mixed-precision support, cost per epoch, and workload suitability (transformers, CNNs, sparse ops).
HardSystem Design
0 practiced
Architect a distributed training system to run a single 1B-parameter transformer on 1024 GPUs across multiple racks and finish within 24 hours. Requirements include efficient inter-GPU communication, spot-instance friendliness, checkpoint/resume, multi-tenant scheduler, and cost tracking. Describe compute topology, communication strategy, checkpoint staging, scheduler behavior, and cost-accounting approaches.
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