AWS Architecture Blog

Category: HAQM SageMaker

Training a call center fraud detection model for IVR calls with HAQM SageMaker Canvas

This blog post will show you how to use the power of ML to build a fraud-detection model using HAQM SageMaker Canvas, a no-code/low-code ML service that business analysts and domain experts can use to build, train, and deploy ML models without requiring extensive ML expertise.

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Top Architecture Blog Posts of 2024

Well, it’s been another historic year! We’ve watched in awe as the use of real-world generative AI has changed the tech landscape, and while we at the Architecture Blog happily participated, we also made every effort to stay true to our channel’s original scope, and your readership this last year has proven that decision was […]

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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 HAQM API Gateway endpoint with Lambda integration. The Lambda function retrieves the suitable model endpoint from the DynamoDB table and invokes the model via the HAQM 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 HAQM 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, […]

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Top Architecture Blog Posts of 2023

2023 was a rollercoaster year in tech, and we at the AWS Architecture Blog feel so fortunate to have shared in the excitement. As we move into 2024 and all of the new technologies we could see, we want to take a moment to highlight the brightest stars from 2023. As always, thanks to our […]

X-ray images are sent to AWS HealthImaging and an HAQM 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 […]

AI/ML hybrid data access strategy reference architecture

Designing a hybrid AI/ML data access strategy with HAQM SageMaker

Over time, many enterprises have built an on-premises cluster of servers, accumulating data, and then procuring more servers and storage. They often begin their ML journey by experimenting locally on their laptops. Investment in artificial intelligence (AI) is at a different stage in every business organization. Some remain completely on-premises, others are hybrid (both on-premises […]