Create and fine-tune sentence transformers for enhanced classification accuracy

Favorite Sentence transformers are powerful deep learning models that convert sentences into high-quality, fixed-length embeddings, capturing their semantic meaning. These embeddings are useful for various natural language processing (NLP) tasks such as text classification, clustering, semantic search, and information retrieval. In this post, we showcase how to fine-tune a sentence

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Shared by AWS Machine Learning October 30, 2024

Unlock organizational wisdom using voice-driven knowledge capture with Amazon Transcribe and Amazon Bedrock

Favorite Preserving and taking advantage of institutional knowledge is critical for organizational success and adaptability. This collective wisdom, comprising insights and experiences accumulated by employees over time, often exists as tacit knowledge passed down informally. Formalizing and documenting this invaluable resource can help organizations maintain institutional memory, drive innovation, enhance

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Shared by AWS Machine Learning October 30, 2024

Automate document processing with Amazon Bedrock Prompt Flows (preview)

Favorite Enterprises in industries like manufacturing, finance, and healthcare are inundated with a constant flow of documents—from financial reports and contracts to patient records and supply chain documents. Historically, processing and extracting insights from these unstructured data sources has been a manual, time-consuming, and error-prone task. However, the rise of

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Shared by AWS Machine Learning October 29, 2024

Empower your generative AI application with a comprehensive custom observability solution

Favorite Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics.

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Shared by AWS Machine Learning October 29, 2024

Customized model monitoring for near real-time batch inference with Amazon SageMaker

Favorite Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. Examples include financial systems processing transaction data streams, recommendation engines processing user activity data, and computer vision models processing video frames. In these scenarios, customized model monitoring for

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Shared by AWS Machine Learning October 28, 2024

Import data from Google Cloud Platform BigQuery for no-code machine learning with Amazon SageMaker Canvas

Favorite In the modern, cloud-centric business landscape, data is often scattered across numerous clouds and on-site systems. This fragmentation can complicate efforts by organizations to consolidate and analyze data for their machine learning (ML) initiatives. This post presents an architectural approach to extract data from different cloud environments, such as

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Shared by AWS Machine Learning October 28, 2024