Code generation with Large Language Models (LLMs) has helped to increase software developer productivity in coding tasks, but has yet to have significant impact on the tasks of software developers that surround this code. In particular, the challenge of infrastructure management remains an open question. We investigate the ability of an LLM agent to construct infrastructure using the Infrastructure as Code (IaC) paradigm. We particularly investigate the use of a feedback loop that returns errors and warnings on the generated IaC to allow the LLM agent to improve the code. We find that, for each iteration of the loop, its effectiveness decreases exponentially until it plateaus at a certain point and becomes ineffective.

In this blogpost, we will walkthrough how make use of AWS App Runner, a managed container service to deploy an NLP model inference service. This service uses deepset's haystack for a simple Q&A application and a REST API. Anyone with interests in football transfers can make use of the simple Q&A application. To stay updated with the latest news on football transfers, I noticed that I spend a lot scrolling through tons of tweets to stay updated. Transfer news are part of the excitement in football -- fans want to know which players are joining or leaving.

Having an environment capable of delivering Amazon SageMaker notebook instances quickly allows data scientists and business analysts to efficiently respond to organizational needs. Data is the lifeblood of an organization, and analyzing that data efficiently provides useful insights for businesses. A common issue that organizations encounter is creating an automated pattern that enables development teams to launch AWS services. Organizations want to enable their developers to launch resources as they need them, but in a centralized and secure fashion. This post demonstrates how to centralize the management of SageMaker instance notebooks using AWS services including AWS CloudFormation, AWS Serverless Application Model (AWS SAM), AWS Service Catalog, Amazon EventBridge, AWS Systems Manager Parameter Store, Amazon API Gateway, and AWS Lambda.

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Amazon SageMaker Serverless Inference (Preview) was recently announced at re:Invent 2021 as a new model hosting feature that lets customers serve model predictions without having to explicitly provision compute instances or configure scaling policies to handle traffic variations. Serverless Inference is a new deployment capability that complements SageMaker's existing options for deployment that include: SageMaker Real-Time Inference for workloads with low latency requirements in the order of milliseconds, SageMaker Batch Transform to run predictions on batches of data, and SageMaker Asynchronous Inference for inferences with large payload sizes or requiring long processing times. Serverless Inference means that you don't need to configure and manage the underlying infrastructure hosting your models. When you host your model on a Serverless Inference endpoint, simply select the memory and max concurrent invocations. Then, SageMaker will automatically provision, scale, and terminate compute capacity based on the inference request volume.

AWS re:Invent is a learning conference hosted by Amazon Web Services for the global cloud computing community. We are super excited to join you at the 10th annual re:Invent to share the latest from AWS leaders and discover more ways to learn and build. Let's celebrate this milestone, which will be offered in person in Las Vegas (November 29-December 3) and in virtual (November 29–December 10) formats. The health and safety of our customers, and partners remains our top priority and you can learn more about it in health measures page. If you haven't already registered, don't forget to register and save your spot at your favorite sessions. The AWS DevOps and Developer Productivity track at re:Invent offers you with sessions that are combination of cultural philosophies, practices, and tools that increase an organization's ability to deliver applications and services at high velocity.

This is a guest post by Cyrus Wong, an AWS Machine Learning Hero. You can learn more about and connect with AWS Machine Learning Heroes at the community page. On July 29, 2020, AWS announced that Amazon Translate now supports Microsoft Office documents, including .docx, The world is full of bilingual countries and cities like Hong Kong. I find myself always needing to prepare Office documents and presentation slides in both English and Chinese.

In the previous blog post, we showed you how to string together Amazon Transcribe and Amazon Comprehend to be able to conduct sentiment analysis on call conversations from contact centers. Here, we demonstrate how to leverage AWS CloudFormation to automate the process and deploy your solution at scale. The following diagram illustrates architecture that takes uses Amazon Transcribe to create text transcripts of call recordings from contact centers. In this example, we refer to Amazon Connect (cloud-based contact center service), but the architecture could work for any contact center. The following diagram describes the architecture for processing transcribed text by using Amazon Comprehend to conduct Entity, Sentiment and Key Phrases analysis.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.

This is a guest post from my colleagues Naveen Swamy and Joseph Spisak. Machine learning is a field of computer science that enables computers to learn without being explicitly programmed. It focuses on algorithms that can learn from and make predictions on data. Most recently, one branch of machine learning, called deep learning, has been deployed successfully in production with higher accuracy than traditional techniques, enabling capabilities such as speech recognition, image recognition, and video analytics. This higher accuracy comes, however, at the cost of significantly higher compute requirements for training these deep models.