AWS Architecture Blog

Category: Amazon SageMaker

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 […]

AI/ML hybrid data access strategy reference architecture

Designing a hybrid AI/ML data access strategy with Amazon 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 […]

AI-based intelligent document processing engine

Optimizing data with automated intelligent document processing solutions

Many organizations struggle to effectively manage and derive insights from the large amount of unstructured data locked in emails, PDFs, images, scanned documents, and more. The variety of formats, document layouts, and text makes it difficult for any standard Optical Character Recognition (OCR) to extract key insights from these data sources. To help organizations overcome […]

Building event-driven architectures with IoT sensor data

The Internet of Things (IoT) brings sensors, cloud computing, analytics, and people together to improve productivity and efficiency. It empowers customers with the intelligence they need to build new services and business models, improve products and services over time, understand their customers’ needs to provide better services, and improve customer experiences. Business operations become more […]

Solution architecture for automatically processing new images and outputting isolated labels identified through semantic segmentation.

Image background removal using Amazon SageMaker semantic segmentation

Many individuals are creating their own ecommerce and online stores in order to sell their products and services. This simplifies and speeds the process of getting products out to your selected markets. This is a critical key indicator for the success of your business. Artificial Intelligence/Machine Learning (AI/ML) and automation can offer you an improved […]

Basic architecture on how data drift is detected using Amazon SageMaker

Detecting data drift using Amazon SageMaker

As companies continue to embrace the cloud and digital transformation, they use historical data in order to identify trends and insights. This data is foundational to power tools, such as data analytics and machine learning (ML), in order to achieve high quality results. This is a time where major disruptions are not only lasting longer, […]

Figure 2. Credit application – technical solution using Amazon SageMaker and Experian CaaS ML models

How Experian uses Amazon SageMaker to Deliver Affordability Verification 

Financial Service (FS) providers must identify patterns and signals in a customer’s financial behavior to provide deeper, up-to-the-minute, insight into their affordability and credit risk. FS providers use these insights to improve decision making and customer management capabilities. Machine learning (ML) models and algorithms play a significant role in automating, categorising, and deriving insights from […]

Figure 3. FL prototype deployed on Amazon ECS Fargate containers and AWS IoT Greengrass cores.

Applying Federated Learning for ML at the Edge

Federated Learning (FL) is an emerging approach to machine learning (ML) where model training data is not stored in a central location. During ML training, we typically need to access the entire training dataset on a single machine. For purposes of performance scaling, we divide the training data between multiple CPUs, multiple GPUs, or a […]