1 days.


We recommend that attendees of this course have a basic understanding of:

  • A basic understanding of ML processes
  • Knowledge of AWS core services like Amazon EC2 and AWS SDK
  • Knowledge of a scripting language like Python

Skills Gained

This course is designed to teach you how to:

  • Learn how to define machine learning (ML) and deep learning
  • Learn how to identify the concepts in a deep learning ecosystem
  • Use Amazon SageMaker and the MXNet programming framework for deep learning workloads
  • Fit AWS solutions for deep learning deployments

Who Can Benefit?

This course is intended for:

  • Developers who are responsible for developing deep learning applications
  • Developers who want to understand the concepts behind deep learning and how to implement a deep learning solution on AWS Cloud

Outline for Deep Learning on AWS Training

Day One

Module 1: Machine learning overview

  • A brief history of AI, ML, and DL
  • The business importance of ML
  • Common challenges in ML
  • Different types of ML problems and tasks
  • AI on AWS

Module 2: Introduction to deep learning

  • Introduction to DL
  • The DL concepts
  • A summary of how to train DL models on AWS
  • Introduction to Amazon SageMaker
  • Hands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model

Module 3: Introduction to Apache MXNet

  • The motivation for and benefits of using MXNet and Gluon
  • Important terms and APIs used in MXNet
  • Convolutional neural networks (CNN) architecture
  • Hands-on lab: Training a CNN on a CIFAR-10 dataset

Module 4: ML and DL architectures on AWS

  • AWS services for deploying DL models (AWS Lambda, AWS IoT Greengrass, Amazon ECS, AWS Elastic Beanstalk)
  • Introduction to AWS AI services that are based on DL (Amazon Polly, Amazon Lex, Amazon Rekognition)
  • Hands-on lab: Deploying a trained model for prediction on AWS Lambda