Machine Learning Pipeline
A Machine Learning Pipeline is a sequence of data processing components that are assembled together to implement a machine learning workflow. It involves a series of steps such as data collection, data preprocessing, feature engineering, model selection, hyperparameter tuning, model training, and model evaluation. The pipeline is designed to automate the entire process of building and deploying machine learning models. It helps to streamline the workflow, reduce errors, and improve the efficiency of the machine learning process. The pipeline can be customized based on the specific requirements of the problem at hand and the data available.
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