AWS ML Engineer Associate 2.1 Choose a Modeling Approach

Resumen

Explore the AWS ML stack layers and learn how to solve common business challenges with AWS services. This course explores how to use Amazon SageMaker for machine learning tasks and how to review strategies for selecting appropriate models.

Additionally, this course highlights specific scenarios for pre-trained Amazon SageMaker JumpStart ML solutions and instructs how to optimize selections for business needs.

This course will also discuss specific applications for pre-trained Amazon Bedrock ML solutions and how to identify the most appropriate built-in solution. Finally, this course explores the importance of interpretability in model and algorithm selection.

• Course level:

  Advanced

• Duration:

  1.5 hours

Activities

• Online materials
• Exercises
• Knowledge check questions

Course objectives

• Articulate the benefits and use cases for each layer of the AWS machine learning stack.
• Explain how AWS artificial intelligence (AI) services help solve common business problems.
• Select AWS AI services to solve common business needs.
• Describe the benefits of using SageMaker for machine learning.
• Identify specific use cases for built-in SageMaker algorithms.
• Select the most appropriate machine learning model algorithms to solve common business needs.
• Identify specific use cases for pre-trained SageMaker JumpStart machine learning solutions.
• Choose the most appropriate SageMaker JumpStart built-in machine learning solution to solve common business needs.
• Describe the role interpretability plays during model or algorithm selection.
• Select the most cost-efficient models or algorithms to solve common business needs.

Intended audience

• Cloud architects
• Machine learning engineers

Recommended Skills

• Completed at least 1 year of experience using SageMaker and other AWS services for ML engineering
• Completed at least 1 year of experience in a related role such as backend software developer, DevOps developer, data engineer, or data scientist
• A fundamental understanding of programming languages such as Python
• Completed preceding courses in the AWS ML Engineer Associate Learning Plan

Course outline

Section 1:

Introduction

• Lesson 1:

  How to Use This Course

• Lesson 2:

  Domain 2 Introduction

• Lesson 3:

  Course Overview

• Lesson 4:

  Choosing a Modeling Approach

Section 2:

Modeling Approaches

• Lesson 5:

  AWS AI Services

• Lesson 6:

  Amazon SageMaker Built-In Algorithms

• Lesson 7:

  Amazon SageMaker JumpStart ML Solutions

• Lesson 8:

  Amazon Bedrock ML Solutions

• Lesson 9:

  Model Selection Considerations

Section 3:

Conclusion

• Lesson 10:

  Course Summary

• Lesson 11:

  Assessment

• Lesson 12:

  Contact Us

Materias