Amazon SageMaker Studio helps data scientists prepare, build, train, deploy, and monitor machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. This course prepares experienced data scientists to use the tools that are part of SageMaker Studio to improve productivity at every step of the ML lifecycle.
Course Objectives
In this course, you will learn how to:
- Accelerate the preparation, building, training, deployment, and monitoring of ML solutions for tabular data by using Amazon SageMaker Studio.
Intended Audience
This course is intended for:
- Experienced data scientists who are proficient in ML and deep learning fundamentals. Relevant experience includes using ML frameworks, Python programming, and the process of building, training, tuning, and deploying models.
Prerequisites
We recommend that attendees of this course have taken the following prerequisite course(s):
- AWS Technical Essentials
- AWS Technical Essentials
We recommend students who are not experienced data scientists complete the following two courses followed by 1-year on-the-job experience building models prior to taking this course:
- Machine Learning Pipeline on AWS (3–day AWS Digital Classroom course)
- Deep Learning on AWS (1–day AWS instructor led course)
Outline
Course Welcome
Module 1 - Amazon SageMaker Setup and Navigation
Module 2 - Data Processing
Module 3 - Model Development
Module 4 – Deployment and Inference
Module 5 - Monitoring
Module 6 - Managing SageMaker Resources and Updates
Course Summary and Resources
