In this course, you’ll learn about AWS’s deep learning solutions, including scenarios where deep learning makes sense and how deep learning works. You’ll learn how to run deep learning models on the cloud using Amazon SageMaker and the MXNet framework. You’ll also learn to deploy your deep learning models using services like AWS Lambda while designing intelligent systems on AWS.


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

Learning Objectives

This course is designed to teach you how to:

Define machine learning (ML) and deep learning
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

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


Length: 1.0 day (8 hours)


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