The Introduction to SageMaker for Data Analysts training course teaches data analysts the ability to build, train, and deploy Machine Learning (ML) models quickly. It gives analysts insight into SageMaker as a fully-managed service that covers the entire ML workflow.
The course begins with students learning the ability to label and prepare data, choosing an algorithm, and training the algorithm. Next, the course covers how to tune and optimize the algorithm for deployment, make predictions, and take action. The course concludes with a lesson on forecasting.
This course includes a hands-on investigation of datasets as well as plotting data with Amazon SageMaker. Course participants should have a familiarity with programming in at least one programming language and a basic familiarity with AWS.
Purpose
|
Learn how to choose the right questions to ask and how to answer them with ML. |
Audience
|
Developers who need to learn SageMaker for ML. |
Role
| Business Analyst - Data Engineer - Data Scientist - Software Developer |
Skill Level
| Introduction |
Style
| Workshops |
Duration
| 1 Day |
Related Technologies
| AWS | SageMaker |
Productivity Objectives
- Describe how Machine Learning differs from business analytics
- Discern forecasting and how it is done in SageMaker
- Apply skills learned to predictive analysis (price prediction)