AWS Architecture Blog
Category: Artificial Intelligence
Real-Time In-Stream Inference with AWS Kinesis, SageMaker, & Apache Flink
As businesses race to digitally transform, the challenge is to cope with the amount of data, and the value of that data diminishes over time. The challenge is to analyze, learn, and infer from real-time data to predict future states, as well as to detect anomalies and get accurate results. In this blog post, we’ll […]
Fast and Cost-Effective Image Manipulation with Serverless Image Handler
As a modern company, you most likely have both a web-based and mobile app platform to provide content to customers who view it on a range of devices. This means you need to store multiple versions of images, depending on the device. The resulting image management can be a headache as it can be expensive […]
Field Notes: Gaining Insights into Labeling Jobs for Machine Learning
In an era where more and more data is generated, it becomes critical for businesses to derive value from it. With the help of supervised learning, it is possible to generate models to automatically make predictions or decisions by leveraging historical data. For example, image recognition for self-driving cars, predicting anomalies on X-rays, fraud detection […]
AWS Architecture Monthly Magazine: Robotics
September’s issue of AWS Architecture Monthly issue is all about robotics. Discover why iRobot, the creator of your favorite (though maybe not your pet’s favorite) little robot vacuum, decided to move its mission-critical platform to the serverless architecture of AWS. Learn how and why you sometimes need to test in a virtual environment instead of […]
Field Notes: Redacting Personal Data from Connected Cars Using Amazon Rekognition
Cameras mounted in connected cars may collect a variety of video data. Organizations may need to redact the personal information (e.g. human faces and automobile license plates) contained in the collected video data in order to protect individuals’ privacy rights and, where required, meet compliance obligations under privacy regulations such as General Data Protection Regulation […]
AWS Architecture Monthly Magazine: Agriculture
In this month’s issue of AWS Architecture Monthly, Worldwide Tech Lead for Agriculture, Karen Hildebrand (who’s also a fourth generation farmer) refers to agriculture as “the connective tissue our world needs to survive.” As our expert for August’s Agriculture issue, she also talks about what role cloud will play in future development efforts in this […]
Field Notes: Inference C++ Models Using SageMaker Processing
Machine learning has existed for decades. Before the prevalence of doing machine learning with Python, many other languages such as Java, and C++ were used to build models. Refactoring legacy models in C++ or Java could be forbiddingly expensive and time consuming. Customers need to know how they can bring their legacy models in C++ […]
Building a Self-Service, Secure, and Continually Compliant Environment on AWS
Introduction If you’re an enterprise organization, especially in a highly regulated sector, you understand the struggle to innovate and drive change while maintaining your security and compliance posture. In particular, your banking customers’ expectations and needs are changing, and there is a broad move away from traditional branch and ATM-based services towards digital engagement. With […]
Field Notes: Bring your C#.NET skills to Amazon SageMaker
Amazon SageMaker is a fully managed service that provides developers and data scientists with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the undifferentiated heavy lifting from each step of the machine learning process to make it easier to develop high-quality models. Amazon SageMaker Notebooks are one-click Jupyter Notebooks […]
Building a Scalable Document Pre-Processing Pipeline
In a recent customer engagement, Quantiphi, Inc., a member of the Amazon Web Services Partner Network, built a solution capable of pre-processing tens of millions of PDF documents before sending them for inference by a machine learning (ML) model. While the customer’s use case—and hence the ML model—was very specific to their needs, the pipeline that does […]







