HAQM A2I is now generally available

AWS is excited to announce the general availability of HAQM Augmented AI (HAQM A2I), a new service that makes it easy to implement human reviews of machine learning (ML) predictions at scale. HAQM A2I removes the undifferentiated heavy lifting associated with building and managing expensive and complex human review systems, so you can ensure your ML models produce accurate predictions. HAQM A2I enables humans and machines to do what they do best by easily inserting human judgment into the ML pipeline.

HAQM A2I provides built-in human review workflows for common ML tasks such as content moderation and text extraction from documents, in combination with HAQM Rekognition and HAQM Textract. You can also create your own human review workflows for ML models built with HAQM SageMaker or with any on-premises or cloud tools via its API.

HAQM A2I also gives you the ability to work with your choice of human reviewers. You can use your own reviewers or choose from a workforce of over 500,000 independent contractors who already do ML-related tasks through HAQM Mechanical Turk. If your ML application requires confidentiality or special skills, you can use workforce vendors that are experienced and pre-screened by AWS for quality and security procedures.

HAQM A2I gives you the flexibility to incorporate human reviews based on your specific requirements. You simply set the business rules (how confident an ML model is in its predictions) to decide which predictions to use automatically or route to a human for validation.

When data comes through your ML pipeline, you can decide, based on your requirements, if the prediction meets your minimum confidence threshold. For example, if you want to extract a unique identifier like a Social Security Number (SSN) from numerous documents, they must be absolutely correct for your downstream application to be successful. You might set your threshold high (such as 99%) to achieve desired model accuracy.

Other data in your application might be secondary to the SSN and not require the same level of scrutiny. Therefore, you might set the threshold lower than SSN extraction. HAQM A2I gives you the flexibility to set different threshold levels based on your needs. Additionally, you can choose to randomly sample ML model outputs for human review so you can regularly evaluate if the model is still performing the way you intended.

HAQM A2I also works well with other services. You can use direct integrations with HAQM Textract and HAQM Rekognition, or use a custom workflow in HAQM A2I for human-in-the-loop validation with HAQM Comprehend, HAQM Translate or other AWS AI services. You can also use the HAQM A2I API to add human reviews to any ML application that uses a custom ML model built with HAQM SageMaker or any other on-premises or cloud tool.

How HAQM A2I works with HAQM Textract

The following diagram shows how HAQM A2I integrates with HAQM Textract. Documents go through HAQM Textract and, based on your business rules, HAQM A2I sends low-confidence predictions to humans to review. You can store these results in an HAQM S3 bucket for your client application to use and be confident of their accuracy.

For example, a mortgage application has hundreds of documents associated with the application process. Each page has different information, signatures, and dates, which are scanned and uploaded into your system. At times, these document scans can be low-quality and require humans to review them to make sure the data is accurate and complete. With HAQM A2I, you can process these documents through HAQM Textract and send the low-confidence or hard-to-read documents to a human reviewer.

Other times, the prediction is at an acceptable confidence level for your application. When this occurs, HAQM A2I returns the prediction to the client application immediately, without a human review. You set the thresholds as needed for your use case.

Using HAQM Textract and HAQM A2I

Belle Fleur Technologies, an AWS Partner Network (APN) Advanced Consulting Partner, believes the ML revolution is altering the way we live, work, and relate to one another, and will transform the way every business in every industry operates.

Belle Fleur knew HAQM Textract was the right solution when working with financial institutions. HAQM Textract allowed them to go through vast quantities of documents and extract the relevant data their clients needed. However, they spent significant time reviewing the more nuanced and critical data manually.

Adding HAQM A2I to the equation was a good fit for their customers. HAQM A2I decreased the time spent building human validation and pulled all the relevant extracted data into one place in an easy-to-understand workflow so reviewers could quickly and easily review ML outputs. Tia Dubuisson, President at Belle Fleur, says, “HAQM A2I not only provides us and our customers peace of mind that the more nuanced data extracted is reviewed by humans, but it also helps train and improve our ML models over time through continuous auditing and improvement.”

For more information about other ways our customers are using HAQM A2I in their ML workflows, see the Augmented AI Customer page.

Getting started

To get started, sign in to the HAQM A2I console or search for HAQM Augmented AI on the AWS Management Console. For more information about creating a human review workflow, see Create a Flow Definition.

HAQM A2I is now available in 12 Regions. For more information about regions see AWS Region Table. For more information about getting started for free, see HAQM Augmented AI pricing.

About the Authors

Andrea Morton-Youmans is a Product Marketing Manager on the AI Services team at AWS. Over the past 10 years she has worked in the technology and telecommunications industries, focused on developer storytelling and marketing campaigns. In her spare time, she enjoys heading to the lake with her husband and Aussie dog Oakley, tasting wine and enjoying a movie from time to time.

Anuj Gupta is the Product Manager for HAQM Augmented AI. He focusing on delivering products that make it easier for customers to adopt machine learning. In his spare time, he enjoys road trips and watching Formula 1.