AWS Architecture Blog

Category: Artificial Intelligence

Diagram showing the Amazon Bedrock solution to simplify and automate billing

Simplify and automate bill processing with Amazon Bedrock

This post was co-written with Shyam Narayan, a leader in the Accenture AWS Business Group, and Hui Yee Leong, a DevOps and platform engineer, both based in Australia. Hui and Shyam specialize in designing and implementing complex AWS transformation programs across a wide range of industries. Enterprises that operate out of multiple locations such as […]

Genomics workflows, Part 7: analyze public RNA sequencing data using AWS HealthOmics

Genomics workflows process petabyte-scale datasets on large pools of compute resources. In this blog post, we discuss how life science organizations can use Amazon Web Services (AWS) to run transcriptomic sequencing data analysis using public datasets. This allows users to quickly test research hypotheses against larger datasets in support of clinical diagnostics. We use AWS […]

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Let’s Architect! Learn About Machine Learning on AWS

A data-driven approach empowers businesses to make informed decisions based on accurate predictions and forecasts, leading to improved operational efficiency and resource optimization. Machine learning (ML) systems have the remarkable ability to continuously learn and adapt, improving their performance over time as they are exposed to more data. This self-learning capability ensures that organizations can […]

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Let’s Architect! Discovering Generative AI on AWS

Generative artificial intelligence (generative AI) is a type of AI used to generate content, including conversations, images, videos, and music. Generative AI can be used directly to build customer-facing features (a chatbot or an image generator), or it can serve as an underlying component in a more complex system. For example, it can generate embeddings […]

This visual summarizes the cost prediction and model training processes. Users request cost predictions for future workflow runs on a web frontend hosted in AWS Amplify. The frontend passes the requests to an Amazon API Gateway endpoint with Lambda integration. The Lambda function retrieves the suitable model endpoint from the DynamoDB table and invokes the model via the Amazon SageMaker API. Model training runs on a schedule and is orchestrated by an AWS Step Functions state machine. The state machine queries training datasets from the DynamoDB table. If the new model performs better, it is registered in the SageMaker model registry. Otherwise, the state machine sends a notification to an Amazon Simple Notification Service topic stating that there are no updates.

Genomics workflows, Part 6: cost prediction

Genomics workflows run on large pools of compute resources and take petabyte-scale datasets as inputs. Workflow runs can cost as much as hundreds of thousands of US dollars. Given this large scale, scientists want to estimate the projected cost of their genomics workflow runs before deciding to launch them. In Part 6 of this series, […]

Intelligent search bot for enterprise document management systems

Simplify document search at scale with intelligent search bot on AWS

Enterprise document management systems (EDMS) manage the lifecycle and distribution of documents. They often rely on keyword-based search functionality. However, it increasingly becomes hard to discover documents as such repositories grow to tens of thousands of items. In this blog, we discuss how Amazon Web Services (AWS) built an intelligent search bot on top of […]

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Let’s Architect! Tools for developers

In the software development process, adopting developer tools makes it easier for developers to write code, build applications, and test more efficiently. As a developer, you can use various AWS developer tools for code editing, code quality, code completion, and so on. These tools include Amazon CodeGuru for code analysis, and Amazon CodeWhisper for getting coding recommendations powered by machine learning algorithms.

In this edition of Let’s Architect!, we’ll show you some tools that every developer should consider including in their toolkit.

X-ray images are sent to AWS HealthImaging and an Amazon SageMaker endpoint extracts insights.

Improving medical imaging workflows with AWS HealthImaging and SageMaker

Medical imaging plays a critical role in patient diagnosis and treatment planning in healthcare. However, healthcare providers face several challenges when it comes to managing, storing, and analyzing medical images. The process can be time-consuming, error-prone, and costly. There’s also a radiologist shortage across regions and healthcare systems, making the demand for this specialty increases […]

Technical architecture for implementing multi-lingual semantic search functionality

Content Repository for Unstructured Data with Multilingual Semantic Search: Part 2

Leveraging vast unstructured data poses challenges, particularly for global businesses needing cross-language data search. In Part 1 of this blog series, we built the architectural foundation for the content repository. The key component of Part 1 was the dynamic access control-based logic with a web UI to upload documents. In Part 2, we extend the […]