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Machine Learning Pipelines

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May 1, 2024 Updated June 21, 2025 21 minute read

An Introduction to Machine Learning Pipelines

Machine Learning (ML) Pipelines are a cornerstone of modern data science, providing a structured and automated approach to the complex process of developing, deploying, and maintaining machine learning models. At a high level, an ML pipeline is a sequence of connected steps that transform raw data into a trained and deployable model. This systematic workflow allows data scientists and engineers to manage the entire lifecycle of an ML project efficiently. Think of it as an assembly line for machine learning: raw materials (data) enter at one end, undergo a series of transformations and processes, and emerge as a finished product (a predictive model) at the other.

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Reading list

We've selected three books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Machine Learning Pipelines.
Provides a comprehensive overview of Machine Learning Pipelines, covering the entire process from data ingestion to model deployment. It is particularly valuable for its detailed explanations of pipeline components and best practices.
Provides an extensive guide to building Machine Learning Pipelines in Python. It covers a wide range of topics, from data preparation to model evaluation, and is particularly helpful for Python developers.
While not explicitly focused on Machine Learning Pipelines, this book provides a deep understanding of feature engineering, which crucial part of building effective pipelines.
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