Introduction to Amazon SageMaker Notebooks
AWS Skill Builder
Last updated November 28, 2025
Introduction to Amazon SageMaker Notebooks is a free, one-hour course on Amazon SageMaker notebooks, a serverless, browser-based environment for data analytics and machine learning work. The idea is that the notebook removes the infrastructure to manage — no servers to provision — so you can move straight to exploring data, visualizing it, and developing models. The course covers what the notebooks are and the advantages they bring over a traditional setup, their core features (including a built-in AI agent, the SageMaker Data Agent), how to navigate the interface and its cell types, and the essential data operations — querying, visualization, and interactive tables — that make up everyday data-science work. It also touches on serverless compute and scaling, and on sharing and exporting notebooks, before a guided demonstration shows the environment in use.
What you'll learn
- What SageMaker notebooks are + their advantages
- Serverless ML workflow, no infrastructure to manage
- The built-in AI agent (SageMaker Data Agent)
- Navigating the interface and cell types
- Querying, visualization, interactive tables
- Serverless compute/scaling, sharing/exporting, with a demo
Frequently asked questions about Introduction to Amazon SageMaker Notebooks
Who is Introduction to Amazon SageMaker Notebooks for?
Machine learning engineers and data scientists, already familiar with Amazon SageMaker, who want an orientation to its serverless, browser-based notebook environment.
Is Introduction to Amazon SageMaker Notebooks free?
Yes — Introduction to Amazon SageMaker Notebooks is completely free to take.
What are the prerequisites for Introduction to Amazon SageMaker Notebooks?
Knowledge of Amazon SageMaker recommended beforehand.
Why we suggest this course
A short, focused orientation to a serverless notebook environment for data and machine learning work — what it removes (infrastructure to manage), what it adds (a built-in AI agent and connected data), and how the everyday operations fit together, finishing on a demonstration rather than theory alone. One thing to know: this is an introduction to a single AWS service, so it is vendor-specific by design — what you learn applies to Amazon SageMaker notebooks in particular, though the notebook-based workflow for data science is a general skill.