Course Overview

In this 1 day Instructor-let training, 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.

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Level: Intermediate

Duration: 1 Day

Delivery Type: Instructor-Led Training

Course Objectives

• Define machine learning and Deep Learning
• Identify the concepts in a Deep Learning ecosystem
• Leverage MXNet programming framework for Deep Learning workloads
• Fit AWS solutions for Deep Learning deployments

Prerequisites

Required

• 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)

Who Should Go For This Training?

• 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 Cloud

Course Outline

Day 1

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