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Implement a data loader that batches and shuffles a dataset, and explain efficient input pipelines.

Data loading is where training pipelines silently bottleneck. The signal is a correct shuffle-then-batch iterator plus knowing why prefetching and parallel loading keep the GPU fed.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

Data loading is where training pipelines silently bottleneck. The signal is a correct shuffle-then-batch iterator plus knowing why prefetching and parallel loading keep the GPU fed.

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Next in this trackImplement Byte Pair Encoding (BPE): train the merges and tokenize text.Next in this trackMerge overlapping intervals, and explain the sort-then-sweep pattern.Next in this trackImplement k-nearest-neighbors classification from scratch, and make prediction efficient.

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