How does TensorFlow handle input pipeline bottlenecks?

Input pipelines use prefetching, caching, and parallel mapping to avoid GPU starvation.

If data loading is slow, GPUs remain idle. TensorFlow uses tf.data API to parallelize loading, apply transformations, and prefetch batches. This ensures compute and data pipeline overlap.