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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.

Familiarising 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 the Amazon Web Services (AWS) Console.

Audience

  • Data scientists.
  • Developers.
 21 Hours

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