1 - Module 1: Machine learning overview
A brief history of AI, ML, and DLThe business importance of MLCommon challenges in MLDifferent types of ML problems and tasksAI on AWS
2 - Module 2: Introduction to deep learning
Introduction to DL The DL conceptsA summary of how to train DL models on AWSIntroduction to Amazon SageMakerHands-on lab: Spinning up an Amazon SageMaker notebook instance and running a multi-layer perceptron neural network model
3 - Module 3: Introduction to Apache MXNet
The motivation for and benefits of using MXNet and GluonImportant terms and APIs used in MXNetConvolutional neural networks (CNN) architecture Hands-on lab: Training a CNN on a CIFAR-10 dataset
4 - 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
Actual course outline may vary depending on offering center. Contact your sales representative for more information.
Who is it For?
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:
AWS core services like Amazon EC2 and knowledge of AWS SDK
A scripting language like Python