This course introduces you to cloud-based Deep Learning (DL) solutions on Amazon Web Services (AWS). The training will detail how deep learning is useful and explain its different concepts. This course also teaches you how to run your models on the cloud using Amazon SageMaker, Amazon Elastic Compute Cloud (Amazon EC2)-based Deep Learning Amazon Machine Image (AMI) and MXNet framework. In addition, you will gain a better understanding of deploying your deep learning models using AWS services like AWS Lambda and Amazon EC2 Container Service (Amazon ECS) while designing intelligent systems on AWS, based on Deep Learning.


* Actual course outline may vary depending on offering center. Contact your sales representative for more information.

Learning Objectives

This course teaches you how to:

Define machine learning and deep learning.
Identify the concepts in a deep learning ecosystem.
Leverage Amazon SageMaker and MXNet programming framework for deep learning workloads
Fit AWS solutions for deep learning deployments

  • 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


This course is intended for: Developers responsible for developing Deep Learning applications Developers who want to understand concepts behind Deep Learning and how to implement a Deep Learning solution on AWS




We recommend that attendees of this course have the following prerequisites: Basic understanding of machine learning processes Basic understanding of AWS core services like Amazon EC2 and knowledge of AWS SDK Basic knowledge of a scripting language e.g. Python


Length: 1.0 day (8 hours)


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