AWS SageMaker — From Fundamentals to Mastery
Use AWS SageMaker in practical AI, data science and model development workflows. Follow 40 topic-specific lessons organized into four levels, with reproducible Python labs and exact captured outputs. Where actual AWS SageMaker API examples were tested, they are labeled as such.
How to learn this course
Begin with the first ten chapters even if you have never used this tool. Work through the sample input and compare your computed answer against the captured output. Intermediate chapters introduce common tasks; advanced chapters focus on validation and system design; mastery chapters cover reliability, scaling, responsible use and an end-to-end capstone.
The Python concept labs are executed as Python, not every named third-party library. A tested library API lab is identified explicitly. Review maintained documentation when using cloud services or APIs that change.