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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. For more details about how to run graph multi-task learning with GraphStorm, refer to Multi-task Learning in GraphStorm in our documentation. introduces refactored graph ML pipeline APIs.

APIs 119
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Knowledge Bases in Amazon Bedrock now simplifies asking questions on a single document

AWS Machine Learning

Today, we’re introducing the new capability to chat with your document with zero setup in Knowledge Bases for Amazon Bedrock. With this new capability, you can securely ask questions on single documents, without the overhead of setting up a vector database or ingesting data, making it effortless for businesses to use their enterprise data.

APIs 128
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How Reveal’s Logikcull used Amazon Comprehend to detect and redact PII from legal documents at scale

AWS Machine Learning

Organizations can search for PII using methods such as keyword searches, pattern matching, data loss prevention tools, machine learning (ML), metadata analysis, data classification software, optical character recognition (OCR), document fingerprinting, and encryption.

APIs 115
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Elevate healthcare interaction and documentation with Amazon Bedrock and Amazon Transcribe using Live Meeting Assistant

AWS Machine Learning

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. By using the solution, clinicians don’t need to spend additional hours documenting patient encounters. What are the differences between AWS HealthScribe and the LMA for healthcare?

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Boosting RAG-based intelligent document assistants using entity extraction, SQL querying, and agents with Amazon Bedrock

AWS Machine Learning

Conversational AI has come a long way in recent years thanks to the rapid developments in generative AI, especially the performance improvements of large language models (LLMs) introduced by training techniques such as instruction fine-tuning and reinforcement learning from human feedback.

Analytics 127
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Dialogue-guided intelligent document processing with foundation models on Amazon SageMaker JumpStart

AWS Machine Learning

Intelligent document processing (IDP) is a technology that automates the processing of high volumes of unstructured data, including text, images, and videos. The system is capable of processing images, large PDF, and documents in other format and answering questions derived from the content via interactive text or voice inputs.

Banking 86
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Elevate workforce productivity through seamless personalization in Amazon Q Business

AWS Machine Learning

Solution overview Responses are personalized by Amazon Q Business by determining if the user’s query could be enhanced by augmenting the query with known attributes of the user and transparently using the personalized query to retrieve documents from its search index. or OIDC is used for the provider.