Automated Machine Learning
May 1, 2024
Updated June 28, 2025
14 minute read
An Introduction to Automated Machine Learning
Automated Machine Learning, often abbreviated as AutoML, represents a significant evolution in the field of artificial intelligence. At its core, AutoML is the process of automating the time-consuming, iterative tasks of machine learning model development. This allows data scientists, analysts, and developers to build high-performing models more efficiently by automating tasks like data preparation, feature engineering, model selection, and hyperparameter tuning. The primary objective is to make machine learning more accessible to a broader audience, regardless of their level of expertise, and to accelerate the path from raw data to actionable insights.
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Reading list
We've selected 22 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
Automated Machine Learning.
This open access book seminal work in the field of AutoML, offering a comprehensive look at its methods, systems, and the challenges it addresses. It's an essential read for anyone serious about understanding the foundations and current state of AutoML. It is often referenced in research and academic discussions.
This highly influential textbook provides a comprehensive overview of statistical learning methods, including supervised and unsupervised learning, regression, and classification models. It valuable resource for understanding the theoretical foundations of AutoML.
Offers a practical approach to AutoML using the AutoKeras and KerasTuner libraries. It's designed to help users automate the process of building and tuning machine learning models, particularly deep learning models. It's a valuable resource for those looking for hands-on experience with specific AutoML tools.
Reinforcement learning subfield of machine learning that is closely related to AutoML. This classic textbook provides a comprehensive introduction to the principles and algorithms of reinforcement learning, making it a valuable resource for understanding the foundations of AutoML.
This practical guide provides a hands-on introduction to AutoML, focusing on automating machine learning workflows using Python libraries. It's suitable for practitioners looking to quickly implement AutoML techniques in their projects. The book includes practical examples and guidance on using popular AutoML tools.
Provides a practical, code-centric introduction to AutoML using Python. It guides readers through implementing AutoML pipelines with popular libraries and applying them to various ML tasks. It's a suitable resource for practitioners and students who want to gain hands-on experience with AutoML in Python.
For those working within the Microsoft Azure ecosystem, this book provides a focused guide on leveraging Azure Machine Learning's AutoML capabilities. It details how to quickly build and deploy AI solutions using the platform, making it highly relevant for professionals in an Azure environment.
This French-language book provides a comprehensive overview of AutoML. It covers topics such as AutoML algorithms, AutoML systems, and AutoML applications, making it a valuable resource for French-speaking readers.
This Spanish-language book provides a comprehensive overview of AutoML. It covers topics such as AutoML algorithms, AutoML systems, and AutoML applications, making it a valuable resource for Spanish-speaking readers.
This practical guide introduces deep learning concepts and techniques using the fastai library and PyTorch framework. It provides hands-on examples and exercises, making it suitable for those looking to gain a practical understanding of deep learning, which fundamental component of AutoML.
Focuses on Metalearning, a field closely related to AutoML, particularly in the context of algorithm selection and hyperparameter optimization. It provides insights into how systems can learn to learn, which core concept in building effective AutoML systems.
A focused book on hyperparameter tuning in Python, this resource is valuable for understanding a key aspect of AutoML. It explores different tuning techniques and frameworks, providing practical guidance for optimizing ML models. It's a good supplementary read for those interested in the technical details of tuning.
Focusing on hyperparameter tuning with R, this book offers a practical perspective on optimizing ML and DL models. While R-specific, the principles of hyperparameter tuning are universally applicable in AutoML. It serves as a useful resource for understanding this critical step in the automated ML pipeline.
Written by a leading expert in machine learning, this book offers a concise overview of the field. It covers topics such as supervised learning, unsupervised learning, and deep learning, providing a solid foundation for understanding AutoML.
This guide provides a broad overview of Automated Machine Learning, covering various aspects and its applications. While potentially less in-depth than specialized texts, it can serve as a good starting point for gaining a general understanding of the topic and its scope.
Considered a foundational text in machine learning and statistics, this book provides the underlying principles for many AutoML techniques. While not explicitly about AutoML, its comprehensive coverage of statistical learning is essential for a deep theoretical understanding of the field. It classic reference for researchers and advanced students.
This practical guide introduces machine learning concepts and techniques in a straightforward and accessible manner. It provides hands-on exercises and examples, making it a great choice for beginners or those looking to gain a practical understanding of machine learning.
This classic machine learning textbook provides a strong theoretical foundation, particularly in probabilistic methods. Understanding these fundamentals is beneficial for comprehending how AutoML systems work and their limitations. It's a valuable resource for those seeking a rigorous introduction to the mathematical underpinnings of ML.
Offers a theoretical foundation in machine learning, covering the essential concepts and algorithms. A solid theoretical background is helpful for understanding the principles behind AutoML methods and evaluating their effectiveness. It's suitable for students seeking a deeper dive into ML theory.
While not specifically focused on AutoML, this book provides a foundation in Python programming, which is essential for implementing AutoML solutions. It teaches practical skills for automating tasks, making it a valuable resource for those new to programming or looking to enhance their Python abilities.
As deep learning major application area for AutoML, this foundational text is highly relevant for understanding the models and architectures that AutoML systems often work with. While challenging, mastering the concepts in this book provides a strong basis for advanced work in AutoML, particularly in areas like Neural Architecture Search.
Focuses on the strategic aspects of building machine learning systems. While not directly about AutoML, it provides valuable context on the challenges in ML development that AutoML aims to solve. It's a good resource for understanding the workflow and decision-making processes in ML projects.
For more information about how these books relate to this course, visit:
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