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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.
Benefits
Extend the modular architecture
Based on the features required for your use case, you can configure the root template to deploy some or all of the nested templates.
Customize the workflow orchestration
Choose from out-of-the-box workflow configuration definitions.
Use artificial intelligence (AI) and machine learning (ML) automation
Get insights from AWS managed AI services, even if you have little or no knowledge or training in deploying ML models.
Text extraction
Use Amazon Textract to pull text and structural information from files and use Amazon Comprehend and Amazon Comprehend Medical for deeper analysis.
Step 1 The user requests the browser to navigate to an Amazon CloudFront URL.
Step 2 The user interface (UI) prompts the user for authentication, which the AWS Solution validates using Amazon Cognito.
Step 3 The UI interacts with the REST endpoint deployed on Amazon API Gateway.
Step 4 The user creates a case that the AWS Solution stores in the Case management storeAmazon DynamoDB table.
Step 5 The user requests a signed Amazon Simple Storage Service (Amazon S3) URL to upload documents to an S3 bucket.
Step 6 Amazon S3 generates an s3:PutObject event on the default Amazon EventBridge event bus.
Step 7 The s3:PutObject event invokes the workflow orchestrator AWS Lambda function. This function uses the configuration stored in the Configuration for orchestrating workflowsDynamoDB table to determine the workflows to be called.
Step 8 The workflow orchestrator Lambda function creates an event and sends it to the custom event bus.
Step 9 The custom event bus invokes one of the three AWS Step Functionsstate machine workflows based on the event definition.
Step 10 The workflow completes and publishes an event to the custom EventBridge event bus.
Step 11 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.
Step 12 (Optional) The workflow orchestrator Lambda function writes metadata from the processed information to an Amazon Kendra index. This index provides the ability to perform ML-powered search.
Step 7 The s3:PutObject event invokes the workflow orchestrator AWS Lambda function. This function uses the configuration stored in the Configuration for orchestrating workflowsDynamoDB table to determine the workflows to be called.
Step 8 The workflow orchestrator Lambda function creates an event and sends it to the custom event bus.
Step 9 The custom event bus invokes one of the three AWS Step Functionsstate machine workflows based on the event definition.
Step 10 The workflow completes and publishes an event to the custom EventBridge event bus.
Step 11 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.
Step 12 (Optional) The workflow orchestrator Lambda function writes metadata from the processed information to an Amazon Kendra index. This index provides the ability to perform ML-powered search.
Step 1 The user requests the browser to navigate to an Amazon CloudFront URL.
Step 2 The user interface (UI) prompts the user for authentication, which the AWS Solution validates using Amazon Cognito.
Step 3 The UI interacts with the REST endpoint deployed on Amazon API Gateway.
Step 4 The user creates a case that the AWS Solution stores in the Case management storeAmazon DynamoDB table.
Step 5 The user requests a signed Amazon Simple Storage Service (Amazon S3) URL to upload documents to an S3 bucket.
Step 6 Amazon S3 generates an s3:PutObject event on the default Amazon EventBridge event bus.
Step 7 The s3:PutObject event invokes the workflow orchestrator AWS Lambda function. This function uses the configuration stored in the Configuration for orchestrating workflowsDynamoDB table to determine the workflows to be called.
Step 8 The workflow orchestrator Lambda function creates an event and sends it to the custom event bus.
Step 9 The custom event bus invokes one of the three AWS Step Functionsstate machine workflows based on the event definition.
Step 10 The workflow completes and publishes an event to the custom EventBridge event bus.
Step 11 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.
Step 12 (Optional) The workflow orchestrator Lambda function writes metadata from the processed information to an Amazon Kendra index. This index provides the ability to perform ML-powered search.
Step 1 The user requests the browser to navigate to an Amazon CloudFront URL.
Step 2 The user interface (UI) prompts the user for authentication, which the AWS Solution validates using Amazon Cognito.
Step 3 The UI interacts with the REST endpoint deployed on Amazon API Gateway.
Step 4 The user creates a case that the AWS Solution stores in the Case management storeAmazon DynamoDB table.
Step 5 The user requests a signed Amazon Simple Storage Service (Amazon S3) URL to upload documents to an S3 bucket.
Step 6 Amazon S3 generates an s3:PutObject event on the default Amazon EventBridge event bus.
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Introduction to Artificial Intelligence
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.