Overview
In this one-day Deep Learning on AWS course, you’ll learn cloud-based deep learning solutions on the AWS platform. You’ll learn how to run your models on the cloud using Amazon EC2‒based deep learning Amazon Machine Image (AMI) and Apache MXNet on AWS frameworks.
In addition, you’ll learn how to use Amazon SageMaker and deploy your deep learning models using AWS services like AWS Lambda and Amazon Elastic Container Service (Amazon ECS), all while designing intelligent systems on AWS.
Kornerstone is an AWS Authorized Training Partner trusted by AWS to offer, deliver, and/or incorporate official AWS Training, including classroom and digital offerings. Whether your team prefers to learn from live instructors, on-demand courses, or both, ATPs offer a breadth of AWS Training options for learners of all levels.
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Why Choose KORNERSTONE
- Accredited, practitioner-led, expert certificate trainers to provide high quality training
- Official training material included
- Practical, scenario-based learning – focus on application, not just theory
- Excellent Passing Rate
Skills Covered
In this course, you will learn how to:
- Define machine learning (ML) and deep learning
- Identify the concepts in a deep learning ecosystem
- Leverage Amazon SageMaker and MXNet programming frameworks for deep learning workloads
- Fit AWS solutions for deep learning deployments
Prerequisites
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 SDKs
- Basic knowledge of a scripting language, such as Python
Target Audience
Who Should Attend
- Developers who are 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
Course Curriculum
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 multilayer 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
Dates & Locations
September 4, 2026
December 4, 2026
Exam & Certification
There is no exam directly associated with this course. However, AWS offers an extensive portfolio of industry-recognized certifications that can help you stand out as a tech professional in 2026 and beyond. Achieving AWS credentials is one of the most effective ways to validate your skills and accelerate your career.
With our expert-led AWS training, you’ll be prepared to:
- Master in-demand capabilities across Cloud, Data & AI, and Cybersecurity — areas driving global digital transformation.
- Prove your expertise with a globally respected credential recognized by employers worldwide.
- Advance your career by enhancing your credibility, increasing your earning potential, and opening doors to new opportunities.
Explore our top AWS Certifications for 2026 and start building the skills that matter today.
