Summary of AWS blogs for the week of Mon Sep 18

In the week of Mon Sep 18 2023 AWS published 97 blog posts – here is an overview of what happened.

Topics Covered

Desktop and Application Streaming

How Intuit Leverages Amazon AppStream 2.0 to Simplify Migration to QuickBooks Online

Intuit, a financial software company, is leveraging Amazon AppStream 2.0 to make it easier for QuickBooks Desktop users to migrate to QuickBooks Online. This blog post, co-authored by Salil Sinai Cuncoliencar, Staff Software Engineer at Intuit, and Manoj Kale and Upendra V at AWS, will explain how Intuit has customized the Amazon AppStream 2.0 experience to simplify the end user experience of migration.

Creating a Simple Migration Experience

Intuit needed to provide a simple migration experience to their customers, allowing users of QuickBooks Desktop to access QuickBooks Online with minimal disruption. Amazon AppStream 2.0 allowed them to customize the streaming experience to meet their customer’s needs.

Intuit took advantage of AppStream 2.0’s ability to deliver full desktop applications and create custom images. This enabled the company to stream QuickBooks Desktop to allow customers to move their data to QuickBooks Online with minimal disruption.

Creating Customized User Experiences

The AppStream 2.0 platform also allowed Intuit to customize the user experience with features like intelligent mouse movement and support for multiple languages. This enabled Intuit to provide a better and more personalized experience for their customers.

In addition, AppStream 2.0 allowed Intuit to quickly and easily deploy their streaming experience in a cost-effective manner. They achieved this by taking advantage of AppStream 2.0’s scalability and elasticity options.

Cloud2 Can Help

At Cloud2, we are experts in Amazon AppStream 2.0 and can help you create a customized streaming experience that meets your customer’s needs. Our team has extensive knowledge of all the features AppStream 2.0 has to offer, and we can help you develop the perfect streaming experience for your customers. Contact us today to learn more.

Read the full blog posts from AWS

AWS DevOps Blog

Importing Existing Resources into AWS CDK Stacks

Many customers have resources created with the AWS Management Console or Infrastructure as Code (IaC) tools, and then later on start using AWS Cloud Development Kit (AWS CDK). To avoid losing data or impacting performance, AWS CDK can be used to import existing resources.

Resource Types

AWS CDK allows for importing resource types such as Amazon S3 buckets, Amazon EC2 instances, AWS CloudFormation stacks, Amazon RDS databases, and Amazon DynamoDB tables. Amazon RDS databases, S3 buckets, and DynamoDB tables can only be imported to a stack if the stack is in the same region as the resource.

Importing Resources with AWS CDK

The syntax used to import resources is the same in all programming languages used to write AWS CDK apps. The following example imports an Amazon EC2 instance and creates an AWS CDK Construct for it, and assigns it to the variable “instance”.

const instance = Instance.fromInstanceAttributes(this, 'Instance', {
instanceId: ec2InstanceId,
instanceType: ec2InstanceType
});

The imported resources can then be used the same way as other AWS CDK Constructs in the stack. This includes creating relationships between resources or attaching policies, as well as importing values such as the instance’s IP address or the S3 bucket’s ARN.

Cloud2 Help

Cloud2’s AWS consultants are highly knowledgeable and experienced in using AWS CDK to help customers manage their resources. We can assist with everything from setting up the initial infrastructure, to importing existing resources, to ongoing management and maintenance. Contact us to learn more.

Read the full blog posts from AWS

Official Machine Learning Blog of Amazon Web Services

Improving Machine Learning with Amazon SageMaker

Amazon SageMaker is an end-to-end machine learning platform that enables customers to quickly and easily build, train, and deploy ML models. With SageMaker, customers can quickly and easily train models on large datasets and take advantage of AWS’s broadest and deepest set of AI/ML capabilities. This blog post explores several ways customers can use Amazon SageMaker to improve their machine learning capabilities.

Improving Your LLMs with RLHF on Amazon SageMaker

In this blog post, we illustrate how reinforcement learning with human feedback (RLHF) can be performed on Amazon SageMaker. We conduct an experiment with the open-sourced RLHF repo Trlx and the publicly available Helpfulness and Harmlessness (HH) dataset provided by Anthropic. Through our experiment, we demonstrate how RLHF can be used to increase the helpfulness or harmlessness of a large language model. We provide a Jupyter notebook for customers to replicate our experiments.

How United Airlines Built a Cost-Efficient Optical Character Recognition Active Learning Pipeline

United Airlines, in

Scroll to Top