Revolutionizing Data Automation with Amazon Bedrock

Amazon Bedrock

Amazon Bedrock is a fully managed AI service that provides access to foundational models (FMs) from various providers. It allows businesses to build and deploy generative AI applications without the complexity of managing infrastructure. Unlike Amazon SageMaker, which is more focused on custom model training and deployment, Bedrock simplifies the integration of pre-trained models into applications, making AI-driven automation more accessible.

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Handling Unstructured Data

One of Amazon Bedrock’s strengths is its ability to process unstructured data such as text, images, video, and even voice inputs. It enables businesses to extract meaningful insights from raw data sources, making it a powerful tool for automating content analysis, customer feedback processing, and document summarization.

Use Cases of Amazon Bedrock

Intelligent Document Processing

Amazon Bedrock Data Automation (BDA) automates intelligent document processing (IDP) at scale, transforming unstructured documents into structured data without manual intervention. Its customizable output integrates seamlessly with existing workflows, enhancing efficiency and accuracy.

Media Analysis

Media and entertainment companies can leverage Bedrock to extract meaningful insights from unstructured video data. Key capabilities include:

Summarizing scenes and identifying key moments.
Detecting and filtering unsafe or explicit content.
Extracting text that appears in videos using OCR capabilities.
Classifying content based on advertisements or brand presence.

Generative AI Assistants

Amazon Bedrock enhances RAG-based AI assistants by extracting rich, modality-specific data from documents, images, videos, and audio, improving contextual understanding and response accuracy.

Amazon Bedrock Data Automation (BDA) is currently available only in the following AWS regions:

US East (N. Virginia) – us-east-1
US West (Oregon) – us-west-2

Output in Amazon Bedrock Automation

Standard Output in Amazon Bedrock Data Automation

Standard output is the default way of interacting with Amazon Bedrock Data Automation (BDA). When you pass a document to the BDA API without specifying a blueprint or project, it returns the default standard output for that file type.

Modification with Projects: Standard output can be customized using projects, which store configuration settings for different data types. Each project can have one standard output configuration per data type.

Always Available: BDA always includes a standard output response, even if a custom output is configured.

Data-Specific Options: Each data type has its standard output options. Some are included by default, while others are optional toggles that can be enabled in a project.

Custom Output and Blueprints

Amazon Bedrock Data Automation allows fine-tuned extractions through custom output configurations using blueprints when working with documents and images.

What are Blueprints? Blueprints define a set of instructions for extracting, transforming, and structuring data from input files.

Integration with Projects: When a file is processed under a project with configured blueprints, BDA applies the relevant blueprint to generate the output.

Multi-Document Processing: BDA can handle up to 40 documents or one image per request when working with blueprints.

Layout Matching: If multiple blueprints exist in a project, BDA automatically assigns the best-fitting blueprint based on the document’s layout.

Steps for Accessing Bedrock Data Automation

Go to the Amazon Bedrock console.
Navigate to the Data Automation section.
Choose between Demo Output or Custom Output based on your requirements.

Using Demo Output

1. Select/Upload File: Upload a File – Select a document, image, video, or audio file to begin processing. The system automatically determines the asset type and processes it accordingly.

step1

Configure and Regenerate Results – Once the initial results are generated, you can fine-tune them using various configuration options like Granularity, Bounding Boxes, Text Format, Generative Fields, and Output Format (JSON or JSON+).
View and Download Results – Results can be viewed at both the Page Level and Element Level. You can also download the processed output.

Similarly, for Custom Output

Upload a Document or Image to start processing.
Create a Blueprint – You can either:

Manually define a Blueprint, specifying the Field Name, Instruction, Data Type, and Extraction Type.
Use a Prompt, allowing Bedrock to auto-generate a blueprint.

step2

3. Generate and Connect the Blueprint – After configuring the blueprint, assign a Project Name and generate it.

step3

4. Retrieve Results – Once the blueprint is connected, click Get Result.

step4

5. Download the Processed File – After generating results, you can download the structured output.

step5

Conclusion

Amazon Bedrock Data Automation simplifies unstructured data processing with AI-powered automation, offering standard and custom outputs for seamless integration. With its intuitive workflow and blueprint-based customization, businesses can efficiently extract, structure, and utilize data at scale.

Drop a query if you have any questions regarding Amazon Bedrock Data Automation and we will get back to you quickly.

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About CloudThat

CloudThat is a leading provider of Cloud Training and Consulting services with a global presence in India, the USA, Asia, Europe, and Africa. Specializing in AWS, Microsoft Azure, GCP, VMware, Databricks, and more, the company serves mid-market and enterprise clients, offering comprehensive expertise in Cloud Migration, Data Platforms, DevOps, IoT, AI/ML, and more.

FAQs

1. What types of data does BDA support?

ANS: – BDA supports multiple data modalities, including text, images, videos, and audio, allowing businesses to extract and structure meaningful insights.

2. What is the difference between Standard Output and Custom Output?

ANS: –

Standard Output: The default response when processing a file without a predefined blueprint or project.
Custom Output: Uses blueprints to define specific extraction rules for customized results.

WRITTEN BY Manjunath Raju S G

Manjunath Raju S G works as a Research Intern at CloudThat. He is enthusiastic about exploring advanced technologies and emerging cloud services, particularly data analytics, machine learning, and cloud computing. In his free time, he enjoys learning new languages to broaden his skill set and staying updated with the latest tech trends and innovations.