Organizations across industries are increasingly required to process large volumes of semi-structured and unstructured documents with greater accuracy and speed. Enhanced Document Understanding on AWS delivers an easy-to-use web application that ingests and analyzes documents, extracts content, identifies and redacts sensitive customer information, and creates search indexes from the analyzed data.
Documents can be uploaded through the web interface for processing. You can optionally enable Amazon Kendra support for machine learning-based enterprise search.
Based on the features required for your use case, you can configure the root template to deploy some or all of the nested templates.
Choose from out-of-the-box workflow configuration definitions.
Get insights from AWS managed AI services, even if you have little or no knowledge or training in deploying ML models.
Use Amazon Textract to pull text and structural information from files and use Amazon Comprehend and Amazon Comprehend Medical for deeper analysis.
The diagram below presents the architecture you can automatically deploy using the solution's implementation guide and accompanying AWS CloudFormation template.
The user interface (UI) prompts the user for authentication, which the AWS Solution validates using Amazon Cognito.
The user creates a case that the AWS Solution stores in the Case management store Amazon DynamoDB table.
The user requests a signed Amazon Simple Storage Service (Amazon S3) URL to upload documents to an S3 bucket.
The s3:PutObject event invokes the workflow orchestrator AWS Lambda function. This function uses the configuration stored in the Configuration for orchestrating workflows DynamoDB table to determine the workflows to be called.
The workflow orchestrator Lambda function creates an event and sends it to the custom event bus.
The workflow completes and publishes an event to the custom EventBridge event bus.
The custom EventBridge event bus invokes the workflow orchestrator Lambda function. This function uses the configuration stored in the Configuration for orchestrating workflows DynamoDB table to determine whether the sequence is complete or if the sequence requires another workflow.
In the course, we discuss what AI is and why it is important, and take a brief look at machine learning and deep learning—which are subsets of AI—and describe how Amazon uses AI in its products.
This course introduces Amazon Machine Learning and Artificial Intelligence tools that enable capabilities across frameworks and infrastructure, machine learning platforms, and API-driven services.