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Machine Learning Libraries
Deep Learning Framework
★ 4.8
End-to-End ML Platform
★ 4.8
pip install torchpip install tensorflowpip install torchpip install tensorflowData engineers building ML data pipelines use PyTorch's `Dataset` and `DataLoader` classes to efficiently feed training data from disk or databases to GPU — defining custom `__getitem__` methods that load, preprocess, and augment data samples. `DataLoader` handles batching, shuffling, and parallel loading transparently.
Python data engineers use TensorFlow's `tf.data` API to build efficient data ingestion pipelines for model training — reading Parquet or TFRecord files, applying transformations in parallel, and batching data for GPU consumption. TFX extends this into a full production ML pipeline with built-in data validation, transformation, and model analysis components.
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