A machine learning pipeline automates the sequence of data processing, model training, and deployment steps. It streamlines workflows by handling data ingestion, feature engineering, and evaluation. Data scientists, engineers, and businesses benefit from faster iterations, reduced errors, and scalable production-ready models. This structured approach ensures consistent, reproducible results for real-world AI applications.
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