Machine Learning Pipelines
A machine learning pipeline is an automated workflow that orchestrates the entire process of taking raw data and transforming it into a deployed machine learning model. It consists of a sequence of interconnected stages, typically including data ingestion, validation, preprocessing, feature engineering, model training, model evaluation, and deployment. By structuring these steps into a cohesive and repeatable process, pipelines enhance efficiency, ensure reproducibility, and provide a scalable framework for managing the complete lifecycle of a machine learning project, bridging the gap between experimental models and production-ready applications.
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2. Core Stages of an ML Pipeline