Design petabyte-scale distributed training
WHAT IT TESTS: end-to-end big-data ML architecture. OUTLINE: object storage with columnar formats, distributed preprocessing, a data-parallel framework with efficient sharded loading, and managed orchestration.
WHAT IT TESTS: whether you can architect training where data volume, not the model, dominates. ANSWER OUTLINE: store raw data in object storage as columnar or sharded formats like Parquet or TFRecord; preprocess with distributed engines such as Spark, writing features back to storage or a feature store; train with a data-parallel framework like PyTorch DDP, Horovod, or DeepSpeed on a GPU cluster, streaming sharded data so input never starves the accelerators; manage infrastructure with managed Kubernetes or a managed training service plus spot…
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- #big-data
- #architecture
- #cloud
- #machine-learning
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