Category: HAQM Augmented AI

This post outlines a custom multilingual document extraction and content assessment framework using a combination of Anthropic’s Claude 3 on HAQM Bedrock and HAQM A2I to incorporate human-in-the-loop capabilities.

In this blog post we are discussing how to accelerate disaster response efforts using computer vision techniques for processing satellite imagery using AWS services.

A massive amount of business documents are processed daily across industries. Many of these documents are paper-based, scanned into your system as images, or in an unstructured format like PDF. Each company may apply unique rules associated with its business background while processing these documents. How to extract information accurately and process them flexibly is […]

HAQM’s intelligent document processing (IDP) helps you speed up your business decision cycles and reduce costs. Across multiple industries, customers need to process millions of documents per year in the course of their business. For customers who process millions of documents, this is a critical aspect for the end-user experience and a top digital transformation […]

Organizations across industries such as healthcare, finance and lending, legal, retail, and manufacturing often have to deal with a lot of documents in their day-to-day business processes. These documents contain critical information that are key to making decisions on time in order to maintain the highest levels of customer satisfaction, faster customer onboarding, and lower […]

In this post, we present a solution for digitizing transactional documents using HAQM Textract and incorporate a human review using HAQM Augmented AI (A2I). You can find the solution source at our GitHub repository. Organizations must frequently process scanned transactional documents with structured text so they can perform operations such as fraud detection or financial […]

The University of Maryland, Baltimore County’s Bina lab is a multidisciplinary research lab for employing advanced computer vision, machine learning (ML), and remote sensing techniques to discover new knowledge of our environment, especially in the Arctic and Antarctic regions. The lab’s work is supported by NSF BIGDATA awards (IIS-1947584, IIS-1838230), the NSF HDR Institute award […]

Proactively detecting unusual or unexpected variances in your business metrics and reducing false alarms can help you stay on top of sudden changes and improve your business performance. Accurately identifying the root cause of deviation from normal business metrics and taking immediate steps to remediate an anomaly can not only boost user engagement but also […]

With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy […]

In Part 1 of this series, we walk through a continuous model improvement machine learning (ML) workflow with HAQM Rekognition Custom Labels and HAQM Augmented AI (HAQM A2I). We explained how we use AWS Step Functions to orchestrate model training and deployment, and custom label detection backed by a human labeling private workforce. We described […]