Introduction
Data extraction and transformation can be challenging for large amounts of data in the form of documents. Traditionally, transforming a document solution uses manual methods. These methods were tedious, time consuming and scalability was also a major challenge.
Amazon web services are fully equipped with multiple generative AI services, such as AWS Bedrock and Amazon Sagemaker.
The advantage of using AWS services is that they can quickly access different document processing services. Amazon Textract, allows you to take advantage of generative artificial intelligence (GenAI) to process and extract data from scanned images.
Textract service has multiple features in it, such as normalization and summarization.
Generative AI generally comprises large machine-learning models known as foundation models (FMs). Foundation models are the backbone of the generative AI used to transform documents in such a way that you can solve them traditionally.
In addition to existing capabilities, different use cases need to be of specific categories of information, including invoices, receipts, financial statements, and reports
FMs are capable of extracting insightful information from the data in documents. We can use their capabilities to automatically extract data from documents, reducing the manual effort required for data extraction.
In today’s era, you need to use manual resources to perform and accomplish these tasks by adding human efforts such as review and complex scripts.
FMs are capable of solving the problems with fewer AWS services, such as AWS bedrock.
What is AWS Bedrock?
At the beginning of the era of generative AI, AWS services such as AWS Bedrock gave us a complete solution to design and streamline the different phases of development and deployment of generative AI-based chatbots.
AWS bedrock is a fully managed one-stop solution for all the basic functionalities of building applications. It works and is compatible with multiple foundation models such as mistral, LLAMA2, LLAMA 3, Cohere, and Stability diffusion to perform multimodal and single-modal tasks.
By using AWS resources, Amazon’s bedrock makes it more suitable for developing multiple applications by using foundation models while combining advanced features of Amazon bedrock and traditional algorithms like NLP and ML.
Document Processing Workloads With AWS Bedrock
In this blog, we will demonstrate and improve the document processing on Amazon’s AWS service bedrock with generative AI.
In the first section, we show how the traditional document processing pipeline can be performed by foundation models and provide a comprehensive step-by-step guide for the use of amazon textract.
The Document processing consists of four parts:
- Classification
- Data extraction
- Refinement
- Integration
Classification
In the Initial step, before going to start the classification part for document processing, we have to gather all the documents and save them into amazon service s3 for storage of the documents.
AWS Bedrock is capable of performing direct communication between the s3 and the foundation models. It means categorizing the documents even if the model has never seen any similar type of examples before performing the classification.
FMs in the data extraction stage perform preprocessing tasks such as normalization of data fields and verifying other data fields while ensuring the formatting.
Data Extraction Pipeline
In the data extraction stage, documents are directly processed in the formats such as PDFs and images.
To process the documents using foundation models available in Amazon Bedrock service, it is necessary to convert them into an appropriate format. These formats are identified by foundation models in the AWS bedrock service. You can use the inbuilt amazon service for extracting the data from the documents.
The architecture of data extraction uses Amazon Textract service for extracting the data and for processing the data using foundation models in Amazon bedrock. The specific type of information may not be processed by FMs in Amazon bedrock. As a result, we can store the information, rectify it, and resend it to the FMs.
Refinement
The document contains structured and unstructured data in the form of raw text, tables, data, and images. Sometimes, foundation models are processed with complete information.
The raw text data and tables data can be extracted from Amazon Textract which plays a crucial role in automating the traditional manual methods.
The document may have multiple table headers in the table data information.
Firstly, it is processed with the Textract document API, if it gives the correct information, it is processed by FMs in the AWS bedrock service. For quick processing of the information that was not extracted properly, we would need to refine and downstream into proper extraction.
Integration
The integration of AWS services, such as foundation models on Amazon Bedrock and Amazon Textract, for document processing uses a combination of all the services to enhance the document processing.
This approach leads to solving the traditional challenges by automating the whole process for classification and extraction.
Benefits Of Automated Content Moderation
Now that we have understood what automated moderation is and how it is helpful for regulatory, safety and operational needs as well as its various use cases, let us now find out the various benefits of automated content moderation.
Regulatory Adherence
Automated content moderation ensures that online platforms comply with regulatory frameworks, leading to the mitigation of legal risks and liabilities for the platforms.
Safety Enhancement
Automated moderation helps to promote a safer online environment for the users by proactively identifying as well as removing harmful content from online platforms.
Operational Efficiency
Automated content moderation helps to streamline moderation workflows leading to optimised resource allocation and improving the scalability of the operations of online platforms.
Customised Compliance
Automated content moderation helps in Customised Compliance Protocols by helping various online platforms to customise the moderation policies according to their specific regulatory requirements and industry standards. As a result, it leads to better consistency and precision across the online platforms.
Multifaceted Analysis
Automated content moderation uses diverse content analysis techniques to comprehensively assess the regulatory compliance and safety integrity of the online platforms.
Integration Flexibility
Automated content moderation seamlessly integrates with existing compliance frameworks helping online platforms in the regulatory adherence process across diverse range of content categories.
Proactive Risk Mitigation
Automated content moderation helps in Proactive Risk Mitigation through continuous monitoring and predictive analytics. As a result, it helps to quickly identify compliance breaches and safety hazards.
With proactive risk mitigation, online platforms are able to minimise several legal risks and operational disruptions.
Continuous Monitoring
Automated content moderation helps in the real-time monitoring of safety compliance and regulatory adherence helping online platforms to quickly take corrective actions when required.
Efficient Incident Response
If an event of safety breach does occur, automated content moderation provides Efficient Incident Response by speeding up the incident response process which minimises the impact on user trust and platform integrity.
With the help of real-time processing capabilities, online platforms are able to quickly address safety incidents in an effective and efficient manner.
Conclusion
Overall, the combination of Amazon bedrock, Textract, and foundation models offers the best solution for document processing workflows. The integration not only improves the overall efficiency but also helps businesses in data-driven insights by automating complex tasks and enhancing information capabilities.
FAQs
FMs are directly incorporated into amazon textract API for document processing by using Amazon Bedrock.
It optimizes the processing time and also performs multiple operations on documents in a very efficient way using Amazon Bedrock service.
FMs streamline document processing using Amazon Bedrock. It incorporates Amazon's multiple services, uses parallel processing, and downstreams the document data.
FMs streamline document processing using Amazon Bedrock. It automatically extracts information from documents and automates the manual, tedious process of generating summaries.
It uses Amazon Bedrock service and foundation models in the backend to process, classify, enhance, and refine the complete pipeline of workloads.
