Course Outline

Introduction

  • Understanding machine learning with SageMaker
  • Machine learning algorithms

Overview of AWS SageMaker Features

  • AWS and cloud computing
  • Models development

Setting up AWS SageMaker

  • Creating an AWS account
  • IAM admin user and group

Familiarizing with SageMaker Studio

  • UI overview
  • Studio notebooks

Preparing Data Using Jupyter Notebooks

  • Notebooks and libraries
  • Creating a notebook instance

Training a Model with SageMaker

  • Training jobs and algorithms
  • Data and model parallel trainings
  • Post-training bias analysis

Deploying a Model in SageMaker

  • Model registry and model monitor
  • Compiling and deploying models with Neo
  • Evaluating model performance

Cleaning Up Resources

  • Deleting endpoints
  • Deleting notebook instances

Troubleshooting

Summary and Conclusion

Requirements

  • Experience with application development
  • Familiarity with Amazon Web Services (AWS) Console

Audience

  • Data scientists
  • Developers
  21 Hours
 

Testimonials



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