AWS ML Engineer Associate Curriculum Overview

סקירה כללית

In this introductory course to the AWS ML Engineer Associate Curriculum, you review machine learning (ML) basics and examine the evolution of ML and AI. You explore the first steps in the ML lifecycle, identifying a business goal and formulating an ML problem based on that business goal.

Finally, you are introduced to Amazon SageMaker, a fully managed AWS service that you can use to build, train, and deploy ML models.

• Course level:

  Advanced

• Duration:

  45 minutes

Activities

• Online materials
• Exercises
• Knowledge check questions

Course objectives

• Define key machine learning components including ML algorithms and models.
• Identify key ML capabilities and algorithms that help solve common business problems.
• Describe how artificial neural networks (ANNs) power deep learning.
• Describe how foundation models (FMs) and large language models (LLMs) power generative AI.
• Identify ways to use ML and AI responsibly.
• Determine the feasibility of an ML solution based on the available data and problem complexity.
• Identify key concepts and benefits of Amazon SageMaker and Amazon SageMaker Studio.

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:

    Curriculum Introduction

  • Lesson 3:

    Course Overview

• Section 2:

  Machine Learning on AWS

  • Lesson 4:

    ML Algorithms and Models

  • Lesson 5:

    Next Generation ML

  • Lesson 6:

    Using AI/ML Responsibly

  • Lesson 7:

    Formulating Business Problems

  • Lesson 8:

    Developing ML Solutions with SageMaker Studio

• Section 3:

  Conclusion

  • Lesson 9:

    Course Summary

  • Lesson 10:

    Contact Us

נושאים