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Machine Learning Libraries
Machine Learning in Python
★ 4.9
End-to-End ML Platform
★ 4.8
pip install scikit-learnpip install tensorflowpip install scikit-learnpip install tensorflowData engineers use scikit-learn Pipelines to build reproducible feature engineering and model training workflows. A `Pipeline` chains `StandardScaler`, `OneHotEncoder`, and a classifier — ensuring the same transformations apply at both training and inference time, preventing data leakage and making model serving straightforward.
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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