Modern Application Development Workshop
This is a workshop to get developers started on AWS using the cloud native primitives. This workshop is designed as a self paced lab which incrementally increases in expertise
We will build a simple web app based on microservices using the 12 factor methodology, deploy it on the cloud and try some debug steps.
We will do this workshop in a pair programming mode. So find a buddy.
The modules build on each other and are intended to be executed linearly.
|Basic||Writing your first AWS Lambda Function on AWS Cloud9||Add a new service using SAM Local to a serverless application which was created by from AWS CodeStar|
|Basic||Deploying Your Function using AWS SAM and AWS CodeDeploy||Deploy Function to Cloud using AWS developer Tools.|
|Basic||Debugging and Monitoring your function||Debug and monitor your application using Cloud9, CodeStar and xRay and Cloudwatch|
|Basic||Build a Continuous Deployment Pipeline||Using AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild, create a continuous deployment pipeline to automatically deploy changes to our application|
|Intermediate||Deploying Deep Learning Functions on Lambda||Predict labels along with their probablities for an image using a pre-trained model with Apache MXNet deployed on AWS Lambda|
|Basic||Getting Started with Amazon ECS using AWS Fargate||Create a new Amazon ECS cluster using the AWS Management Console. At the end of this module, we’ll have a new ECS cluster and supporting infrastructure such as a VPC and subnets and a small Hello World application running|
|Basic||Create a Docker Image Repository||Create a new Docker registry repository for workshop images in Amazon ECR.|
|Basic||Build and Push a Docker Image||Fork a sample application from GitHub which uses an Amazon DynamoDB table to store notable quotations and build it as a Docker container image and push it to your new Docker image repository.|
|Basic||Create a Service||Fork a sample application from GitHub which uses an Amazon DynamoDB table to store notable quotations and build it as a Docker container image and push it to your new Docker image repository.|
|Intermediate||Build a Continuous Deployment Pipeline||Using AWS CodeCommit, AWS CodePipeline, and AWS CodeBuild, create a continuous deployment pipeline to automatically deploy changes to our application|
|Basic||Deploy Java spring PetClining microservice app on ECS||We will be using AWS CodeCommit, AWS CodePipeline, AWS CodeBuild to demonstrate continuous delivery of a Java Spring Boot microservices. We will be using the Spring PetClinic project.|
|Intermediate||Containers - Blue-Green Deployment||Execute a canary deployment for Amazon EC2 Container Service. In order to provide an automated and safe method of migrating traffic from a blue deployment to a green one, this solution leverages Route53 weights to adjust the traffic flow from one ECS service to another.|
|Intermediate||Deploying Deep Learning Functions on ECS||Create an automated workflow that will provision, configure and orchestrate a pipeline triggering deployment of any changes to your AI model or application code.|
|Basic||Deploy a sample application on Kubernetes using AWS CodeSuite Tools||The CodeSuite Continuous Deployment reference architecture demonstrates how to achieve continuous deployment of an application to a Kubernetes cluster using AWS CodePipeline, AWS CodeCommit, AWS CodeBuild and AWS Lambda. We will use a sample puython application.|
|Advanced||Cross Regions/Cross Account Pipeline||Build an automated cross-region code deployment solution using AWS CodePipeline, AWS CodeDeploy , and AWS Lambda|
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