A machine learning pipeline is a structured sequence of data processing and model building steps designed to streamline the development and deployment of machine learning models. It encompasses data ingestion, preprocessing, feature engineering, model training, evaluation, and deployment. The pipeline automates these steps, enhancing efficiency, repeatability, and collaboration in the machine learning workflow.

The importance of a machine learning pipeline lies in its ability to create a systematic and organized approach to model development. It promotes consistency in experimentation, allowing data scientists to iterate quickly and experiment with various models and parameters. Additionally, pipelines facilitate collaboration among team members by providing a standardized framework. They enhance reproducibility, ensuring that experiments are easily repeatable and results are consistent. Overall, a well-structured machine learning pipeline accelerates the model development lifecycle and contributes to the robustness and reliability of machine learning applications.

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