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Intelligent document processing with AWS AI services: Part 2

AWS Machine Learning

Amazon’s intelligent document processing (IDP) helps you speed up your business decision cycles and reduce costs. Across multiple industries, customers need to process millions of documents per year in the course of their business. The following figure shows the stages that are typically part of an IDP workflow.

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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

Knowledge Bases for Amazon Bedrock automates synchronization of your data with your vector store, including diffing the data when it’s updated, document loading, and chunking, as well as semantic embedding. RAG is a popular technique that combines the use of private data with large language models (LLMs).

APIs 115
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Use a generative AI foundation model for summarization and question answering using your own data

AWS Machine Learning

Large language models (LLMs) can be used to analyze complex documents and provide summaries and answers to questions. The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset.

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Automate caption creation and search for images at enterprise scale using generative AI and Amazon Kendra

AWS Machine Learning

Amazon Kendra supports a variety of document formats , such as Microsoft Word, PDF, and text from various data sources. In this post, we focus on extending the document support in Amazon Kendra to make images searchable by their displayed content. This means you can manipulate and ingest your data as needed.

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Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

AWS Machine Learning

Since conversational AI has improved in recent years, many businesses have adopted cutting-edge technologies like AI-powered chatbots and AI-powered agent support to improve customer service while increasing productivity and lowering costs. API Gateway bypasses the request to Lambda. Lambda checks the format and stores it in DynamoDB.

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­­Speed ML development using SageMaker Feature Store and Apache Iceberg offline store compaction

AWS Machine Learning

SageMaker Feature Store automatically builds an AWS Glue Data Catalog during feature group creation. Customers can also access offline store data using a Spark runtime and perform big data processing for ML feature analysis and feature engineering use cases. Table formats provide a way to abstract data files as a table.

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Enable fully homomorphic encryption with Amazon SageMaker endpoints for secure, real-time inferencing

AWS Machine Learning

Leidos is a FORTUNE 500 science and technology solutions leader working to address some of the world’s toughest challenges in the defense, intelligence, homeland security, civil, and healthcare markets. Applications and services can call the deployed endpoint directly or through a deployed serverless Amazon API Gateway architecture.

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