Summary of AWS blogs for the week of Monday Apr 10

In the week of Mon Apr 10, 2023, AWS published 91 blog posts – here is an overview of what happened.

Topics Covered

Official Machine Learning Blog of Amazon Web Services

Machine Learning with Generative AI on AWS

Announcing New Tools for Building with Generative AI on AWS

The use of machine learning (ML) technologies has enabled customers across industries to transform their businesses. Generative AI applications such as ChatGPT have captured widespread attention and imagination. AWS has addressed this trend with several new tools to enable customers to build their own generative AI applications, including Amazon SageMaker Studio, Amazon CodeWhisperer, and Amazon Lookout for Equipment.

How Accenture is using Amazon CodeWhisperer to Improve Developer Productivity

Amazon CodeWhisperer is an AI coding companion that helps improve developer productivity by generating code recommendations based on their comments in natural language and code in the integrated development environment (IDE). With CodeWhisperer, developers can save time by reducing context-switches between their IDE and external documentation or developer forums.

Deploy a Predictive Maintenance Solution for Airport Baggage Handling Systems with Amazon Lookout for Equipment

Delivering baggage on time depends on a massive infrastructure called the baggage handling system (BHS). To keep BHS running, customers need a predictive maintenance solution. Amazon Lookout for Equipment offers an AI-driven tool for predictive maintenance of BHS. It can identify trends and patterns in data to detect potential problems before they occur.

Modulate Makes Voice Chat Safer While Reducing Infrastructure Costs with Amazon EC2 G5g Instances

Modulate is a Boston-based startup on a mission to build richer, safer, more inclusive online gaming experiences for everyone. To achieve this mission, the company is using Amazon EC2 G5g instances. This enables them to reduce their infrastructure costs by a factor of five and enhance the security of their voice chat.

Secure Your Amazon Kendra Indexes with an ACL Using a JWT Shared Secret Key

Organizations have critical business data dispersed across multiple content repositories, making it difficult to access this information in a cohesive manner. Amazon Kendra provides a unified and secure search experience with a wide range of document formats and access control mechanisms. To secure your Amazon Kendra indexes, you can use an access control list (ACL) with a JWT shared secret key.

How Games24x7 Transformed their Retraining MLOps Pipelines with Amazon SageMaker

Games24x7 is one of India’s most valuable multi-game platforms and entertains over 100 million gamers. To enable a vision of end-to-end informatics around game dynamics, game platforms, and players, the company transformed their retraining MLOps pipelines with Amazon SageMaker. This enabled them to automate their workflow and reduce training time from days to hours.

Detect Real and Live Users and Deter Bad Actors Using Amazon Rekognition Face Liveness

Verifying user identity is critical, whether it’s for financial services, the gig economy, telco, healthcare, social networking, or other customers. Amazon Rekognition Face Liveness can detect malicious actors, who attempt to use static images or videos to access an application, by comparing the user’s face in a selfie to a government-issued identity card photo or preestablished profile photo.

Build Streamlit Apps in Amazon SageMaker Studio

With Streamlit, creating demo applications for your ML solution is easy. Streamlit is an open-source Python library that makes it easy to create and share web apps for ML and data science. Amazon SageMaker Studio enables you to quickly launch the Streamlit IDE so you can start building demo applications.

Secure Amazon SageMaker Studio Presigned URLs Part 3: Multi-Account Private API Access to Studio

Enterprise customers have multiple lines of businesses (LOBs) and groups within them. AWS provides a secure, scalable, and flexible MLOps platform within Amazon SageMaker Studio, allowing enterprise customers to balance governance, security, and compliance. With multi-account private API access, users can quickly and securely access their data science environments.

Run Secure Processing Jobs Using PySpark in Amazon SageMaker Pipelines

Amazon SageMaker Pipelines enable you to build a secure, scalable, and flexible MLOps platform within SageMaker Studio. This post explains how to run PySpark processing jobs within a pipeline, enabling anyone that uses SageMaker to quickly and easily deploy secure processing jobs.

Create your RStudio on Amazon SageMaker Licensed or Trial Environment in Three Easy Steps

RStudio on Amazon SageMaker removes the need for you to manage the underlying Posit Workbench infrastructure. With just three easy steps, you can create your RStudio on Amazon SageMaker licensed or trial environment. This enables your teams to concentrate on producing value for your business.

Inpaint Images with Stable Diffusion Using Amazon SageMaker JumpStart

AWS customers can generate images from text with Stable Diffusion models using Amazon SageMaker JumpStart. Now, they can also use this platform to inpaint images with Stable Diffusion models. Inpainting refers to the process of replacing a portion of an image with another image or a new background.

Deploy Large Language Models on AWS Inferentia2 Using Large Model Inference Containers

Large language models (LLMs) are improving applications and tasks such as better search results, image recognition for the visually impaired, creating novel designs from text, and intelligent chatbots. AWS has addressed this trend by introducing the ability to deploy large language models on AWS Inferentia2 using large model inference containers.

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