AWS Architecture Blog

Category: Artificial Intelligence

Training workflow

Classifying Millions of Amazon items with Machine Learning, Part I: Event Driven Architecture

As part of AWS Professional Services, we work with customers across different industries to understand their needs and supplement their teams with specialized skills and experience. Some of our customers are internal teams from the Amazon retail organization who request our help with their initiatives. One of these teams, the Global Environmental Affairs team, identifies […]

reference architecture - build automated scene detection pipeline - Autonomous Driving

Field Notes: Building an automated scene detection pipeline for Autonomous Driving – ADAS Workflow

This Field Notes blog post in 2020 explains how to build an Autonomous Driving Data Lake using this Reference Architecture. Many organizations face the challenge of ingesting, transforming, labeling, and cataloging massive amounts of data to develop automated driving systems. In this re:Invent session, we explored an architecture to solve this problem using Amazon EMR, Amazon […]

Figure 1 - Architecture Showing how to build an automated Image Processing and Model Training pipeline

Field Notes: Building an Automated Image Processing and Model Training Pipeline for Autonomous Driving

In this blog post, we demonstrate how to build an automated and scalable data pipeline for autonomous driving. This solution was built with the goal of accelerating the process of analyzing recorded footage and training a model to improve the experience of autonomous driving. We will demonstrate the extraction of images from ROS bag file […]

High-level design for an AWS lake house implementation

Benefits of Modernizing On-premises Analytics with an AWS Lake House

Organizational analytics systems have shifted from running in the background of IT systems to being critical to an organization’s health. Analytics systems help businesses make better decisions, but they tend to be complex and are often not agile enough to scale quickly. To help with this, customers upgrade their traditional on-premises online analytic processing (OLAP) […]

Architecture showing how to build a Scalable Real-Time Newsfeed Watchlist Using Amazon Comprehend

Field Notes: Building a Scalable Real-Time Newsfeed Watchlist Using Amazon Comprehend

One of the challenges businesses have is to constantly monitor information via media outlets and be alerted when a key interest is picked up, such as individual, product, or company information. One way to do this is to scan media and news feeds against a company watchlist. The list may contain personal names, organizations or […]

Figure 1. Notional architecture for improving forecasting accuracy solution and SAP integration

Improving Retail Forecast Accuracy with Machine Learning

The global retail market continues to grow larger and the influx of consumer data increases daily. The rise in volume, variety, and velocity of data poses challenges with demand forecasting and inventory planning. Outdated systems generate inaccurate demand forecasts. This results in multiple challenges for retailers. They are faced with over-stocking and lost sales, and […]

How to redact confidential information in your ML pipeline

Integrating Redaction of FinServ Data into a Machine Learning Pipeline

Financial companies process hundreds of thousands of documents every day. These include loan and mortgage statements that contain large amounts of confidential customer information. Data privacy requires that sensitive data be redacted to protect the customer and the institution. Redacting digital and physical documents is time-consuming and labor-intensive. The accidental or inadvertent release of personal information […]

Figure 2. Fraud detection using machine learning architecture on AWS

Analyze Fraud Transactions using Amazon Fraud Detector and Amazon Athena

Organizations with online businesses have to be on guard constantly for fraudulent activity, such as fake accounts or payments made with stolen credit cards. One way they try to identify fraudsters is by using fraud detection applications. Some of these applications use machine learning (ML). A common challenge with ML is the need for a […]

Architecture diagram

CohnReznick Automates Claim Validation Workflow Using AWS AI Services

This post was co-written by Winn Oo and Brendan Byam of CohnReznick and Rajeswari Malladi and Shanthan Kesharaju CohnReznick is a leading advisory, assurance, and tax firm serving clients around the world. CohnReznick’s government and public sector practice provides claims audit and verification services for state agencies. This process begins with recipients submitting documentation as […]

Figure 1 - Architecture overview of the solution to launch a fully configured AWS Deep Learning Desktop with NICE DCV

Field Notes: Launch a Fully Configured AWS Deep Learning Desktop with NICE DCV

You want to start quickly when doing deep learning using GPU-activated Elastic Compute Cloud (Amazon EC2) instances in the AWS Cloud. Although AWS provides end-to-end machine learning (ML) in Amazon SageMaker, working at the deep learning frameworks level, the quickest way to start is with AWS Deep Learning AMIs (DLAMIs), which provide preconfigured Conda environments for […]