Course Description
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.
Prerequisites
We recommend that attendees of this course have-
- A basic understanding of ML processes
- Knowledge of AWS core services like Amazon EC2 and AWS SDK
- Knowledge of a scripting language like Python
Target Audience
This course is intended for-
- Developers who want to understand the concepts behind deep learning and how to apply a deep learning solution on AWS Cloud
- Developers who are responsible for developing deep-learning applications
Course Objectives
In this course, you will-
- 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
Content Outline
- 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
- 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
- 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
- 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
FAQs
Amazon Relational Database Service. Amazon RDS is available on numerous database instance types- optimized for memory, performance, or I/O - and presents you with 6 familiar database engines to choose from, including PostgreSQL Amazon Aurora, MySQL, MariaDB, Oracle Database, and SQL Server.
AWS fully managed database services provide continuous monitoring, self-healing storage, and automated scaling to help you focus on application development. Achieve performance at scale.
A data lake is a centralized and secured repository that collects all your data, both in its original form and prepared for analysis.
There are three methods of data storage, namely- –
Object storage
File storage
Block storage
EC2 is a service that enables business clients to run application programs in the computing environment.
AWS security provides opportunities to protect the data, check out security-related activity and receive automated responses.
Radiant believes in a practical and creative approach to training and development, which distinguishes it from other training and development platforms. Moreover, training courses are undertaken by some of the experts who have a vast range of experience in their domain.
Radiant team of experts will be available at e-mail support@radianttechlearning.com to answer your technical queries even after the training program.