A Machine Learning Pipeline is the end-to-end automated workflow for developing, training, validating, deploying, and monitoring ML models. It encompasses data ingestion, preprocessing, feature engineering, model selection, hyperparameter tuning, training, evaluation, deployment, and continuous monitoring, and typically adopts MLOps practices with automated orchestration, versioning, and experiment tracking to ensure reproducibility, scalability, and maintainability of ML systems in production environments.
Semantic Classification
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Key Characteristics
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Automates data preprocessing and feature engineering
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Implements version control for data, code, and models
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Orchestrates distributed training and hyperparameter tuning
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Enables continuous integration and deployment (CI/CD)
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Monitors model performance and triggers retraining
Overview
Machine Learning Pipeline represents the end-to-end workflow for developing, training, validating, deploying, and monitoring ML models. This encompasses data ingestion, preprocessing, feature engineering, model selection, hyperparameter tuning, training, evaluation, deployment, and continuous monitoring. Modern pipelines adopt MLOps practices with automated orchestration (Airflow, Kubeflow), versioning (DVC, MLflow), experiment tracking, A/B testing, and model retraining triggers. Pipelines ensure reproducibility, scalability, and maintainability of ML systems in production environments.
Related Concepts
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References
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Paleyes, A. et al. (2022). Challenges in Deploying Machine Learning: A Survey of Case Studies. ACM Computing Surveys, 55(6), 1-29.
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Baylor, D. et al. (2017). TFX: A TensorFlow-Based Production-Scale Machine Learning Platform. KDD 2017.
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Polyzotis, N. et al. (2018). Data Lifecycle Challenges in Production Machine Learning. SIGMOD Record, 47(2), 17-28.