TensorFlow Datasets (TFDS) is a library providing ready-to-use public datasets for machine learning, primarily integrated with TensorFlow but also supporting JAX and PyTorch. It offers a standardized API (tfds.load, tfds.builder) for downloading, preparing, and loading datasets as tf.data.Dataset objects or numpy arrays. The architecture consists of core components (dataset builders, feature connectors, download management) and hundreds of dataset-specific implementations following a consistent pattern. Key entry points include the tfds CLI for dataset operations and the main tfds module for programmatic access. Data flows involve downloading raw data, generating TFRecord files via builders, and yielding examples through tf.data pipelines. External dependencies include TensorFlow, absl-py, numpy, pandas, and dataset-specific extras like apache-beam for large-scale processing or huggingface_hub for community datasets.