Amazon Web Services (AWS) SageMaker

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

Audience

• Data scientists
• Developers

Amazon Web Services (AWS) SageMaker is a cloud machine learning service that lets developers build, train, and deploy machine learning models quickly at any scale.

This instructor-led, live training (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.

By the end of this training, participants will be able to:

• Use notebook instances to prepare and upload data for training.
• Train machine learning models using training datasets.
• Deploy trained models to an endpoint to create predictions.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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