May 1, 2024
Updated June 29, 2025
12 minute read
AI Platform Pipelines is a cloud-based service that helps data scientists and machine learning engineers build, deploy, and manage end-to-end machine learning pipelines. Pipelines are a sequence of steps that transform raw data into trained models that can be used to make predictions. AI Platform Pipelines makes it easy to create and manage pipelines, and it provides a number of features that can help data scientists and machine learning engineers to improve the performance and efficiency of their pipelines.
Why Learn AI Platform Pipelines?
There are a number of reasons why you might want to learn AI Platform Pipelines. First, AI Platform Pipelines can help you to improve the performance and efficiency of your machine learning pipelines. Pipelines can be used to automate a number of tasks that are typically performed manually, such as data cleaning, feature engineering, and model training. This can free up data scientists and machine learning engineers to focus on more strategic tasks, such as developing new models and improving the performance of existing models.
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Find a path to becoming a AI Platform Pipelines. Learn more at:
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Reading list
We've selected eight 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
AI Platform Pipelines.
While this book does not focus specifically on machine learning pipelines, it provides a deep dive into the theory and practice of machine learning, which is essential knowledge for anyone working with pipelines. The author, Andrew Ng, renowned expert in the field of machine learning and artificial intelligence.
While this book does not focus specifically on machine learning pipelines, it provides a comprehensive overview of the principles and best practices of designing and building data-intensive applications, which is essential knowledge for anyone working with pipelines.
While this book focuses on continuous delivery for machine learning models, it also provides valuable insights into the principles and best practices of pipeline construction and management.
Provides a collection of design patterns for machine learning pipelines, covering various aspects of pipeline construction and management.
While this book focuses on MLflow, a platform for managing machine learning pipelines, it also provides valuable insights into the general principles and best practices of pipeline construction.
While this book does not focus specifically on machine learning pipelines, it provides a comprehensive introduction to machine learning concepts and techniques, which is essential knowledge for anyone working with pipelines.
While this book does not focus specifically on machine learning pipelines, it provides a valuable overview of the business context and applications of data science, which is important for anyone working with pipelines.
Although Apache Airflow is primarily used for data pipelines, this book provides valuable insights into the concepts and techniques of pipeline construction.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/wiitwx/ai
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