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Machine Learning Libraries
High-Level Neural Networks API
★ 4.7
End-to-End ML Platform
★ 4.8
pip install keraspip install tensorflowpip install keraspip install tensorflowData engineers use Keras to prototype and deploy deep learning models quickly. The high-level API reduces boilerplate — defining a model, compiling with a loss and optimizer, and calling `.fit()` with a data pipeline is typically under 20 lines. Keras preprocessing layers are particularly useful for building self-contained models that include their own data transformation logic.
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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