Amazon Web Services (AWS) SageMaker Training Course

319 Phaya Thai Road, Pathum Wan, Pathum Wan District, Bangkok, Thailand

Description

Overview

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.

Course Details :

Course Code : awssagemaker
Duration: 21 hours (usually 3 days including breaks)
Workday courses take place between 09:30 and 16:30
Requirements
– Experience with application development
– Familiarity with Amazon Web Services (AWS) Console
Audience
– Data scientists
– Developers
Fees : 94820 THB(Price per participant)
Venue : Regus, Bangkok

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

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Course Schedule

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