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Scalable intelligent document processing using Amazon Bedrock

AWS Machine Learning

In today’s data-driven business landscape, the ability to efficiently extract and process information from a wide range of documents is crucial for informed decision-making and maintaining a competitive edge. The Anthropic Claude 3 Haiku model then processes the documents and returns the desired information, streamlining the entire workflow.

APIs 127
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Introducing the Amazon SageMaker Serverless Inference Benchmarking Toolkit

AWS Machine Learning

To help determine whether a serverless endpoint is the right deployment option from a cost and performance perspective, we have developed the SageMaker Serverless Inference Benchmarking Toolkit , which tests different endpoint configurations and compares the most optimal one against a comparable real-time hosting instance.

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Cohere Embed multimodal embeddings model is now available on Amazon SageMaker JumpStart

AWS Machine Learning

All text-to-image benchmarks are evaluated using Recall@5 ; text-to-text benchmarks are evaluated using NDCG@10. Text-to-text benchmark accuracy is based on BEIR, a dataset focused on out-of-domain retrievals (14 datasets). Generic text-to-image benchmark accuracy is based on Flickr and CoCo. jpg") or doc.endswith(".png"))

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What to Look for in a Document Automation Tool

Cincom

Manually creating customized communication documents like quotes, invoices, contracts, and reports is an inefficient process prone to human error. If you’re considering implementing a document automation solution for your organization, there are several key capabilities to evaluate during your search. What Is Document Automation?

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Get started with Amazon Titan Text Embeddings V2: A new state-of-the-art embeddings model on Amazon Bedrock

AWS Machine Learning

In September of 2023, we announced the launch of Amazon Titan Text Embeddings V1, a multilingual text embeddings model that converts text inputs like single words, phrases, or large documents into high-dimensional numerical vector representations. In this benchmark, 33 different text embedding models were evaluated on the MTEB tasks.

Benchmark 126
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Fine-tune multimodal models for vision and text use cases on Amazon SageMaker JumpStart

AWS Machine Learning

Vision Instruct models demonstrated impressive performance on the challenging DocVQA benchmark for visual question answering. This dataset consists of a diverse collection of document images paired with a series of natural language questions that require both visual and textual understanding to answer correctly. The Meta Llama 3.2

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Build a RAG-based QnA application using Llama3 models from SageMaker JumpStart

AWS Machine Learning

You can use the BGE embedding model to retrieve relevant documents and then use the BGE reranker to obtain final results. On Hugging Face, the Massive Text Embedding Benchmark (MTEB) is provided as a leaderboard for diverse text embedding tasks. It currently provides 129 benchmarking datasets across 8 different tasks on 113 languages.

APIs 127