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Enhancing AWS intelligent document processing with generative AI

AWS Machine Learning

Data classification, extraction, and analysis can be challenging for organizations that deal with volumes of documents. Traditional document processing solutions are manual, expensive, error prone, and difficult to scale. FMs are transforming the way you can solve traditionally complex document processing workloads.

APIs 74
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Amazon SageMaker model parallel library now accelerates PyTorch FSDP workloads by up to 20%

AWS Machine Learning

In particular, we cover the SMP library’s new simplified user experience that builds on open source PyTorch Fully Sharded Data Parallel (FSDP) APIs, expanded tensor parallel functionality that enables training models with hundreds of billions of parameters, and performance optimizations that reduce model training time and cost by up to 20%.

Scripts 96
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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. It then employs a language model to generate a response by considering both the retrieved documents and the original query.

APIs 110
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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Organizations across industries such as retail, banking, finance, healthcare, manufacturing, and lending often have to deal with vast amounts of unstructured text documents coming from various sources, such as news, blogs, product reviews, customer support channels, and social media. Extract and analyze data from documents.

Scripts 71
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Get smarter search results with the Amazon Kendra Intelligent Ranking and OpenSearch plugin

AWS Machine Learning

wiki, informational web sites, self-service help pages, internal documentation, etc.) The intended meaning of both query and document can be lost because the search is reduced to matching component keywords and terms. Create test indexes, and load some sample documents. Install Docker. If Docker (i.e.,

Scripts 78
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Build an image search engine with Amazon Kendra and Amazon Rekognition

AWS Machine Learning

Using architecture diagrams as an example, the solution needs to search through reference links and technical documents for architecture diagrams and identify the services present. Therefore, users without ML expertise can enjoy the benefits of a custom labels model through an API call, because a significant amount of overhead is reduced.

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Take your intelligent search experience to the next level with Amazon Kendra hierarchical facets

AWS Machine Learning

Amazon Kendra uses deep learning and reading comprehension to deliver precise answers, and returns a list of ranked documents that match the search query for you to choose from. We first ingest a set of documents, along with their metadata, into an Amazon Kendra index. Solution overview.

APIs 70