Remove Auto-classification Remove Auto-complete Remove Natural Language Processing
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Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

AWS Machine Learning Blog

Customers can create the custom metadata using Amazon Comprehend , a natural-language processing (NLP) service managed by AWS to extract insights about the content of documents, and ingest it into Amazon Kendra along with their data into the index. Custom classification is a two-step process.

Metadata 131
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Understanding Graph Neural Network with hands-on example| Part-1

Becoming Human

A typical application of GNN is node classification. GNNs are a hybrid of an information diffusion mechanism and neural networks that are used to process data, representing a set of transition functions and a set of output functions. Graph Classification: The goal here is to categorize the entire graph into various categories.

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

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Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

PyTorch is a machine learning (ML) framework based on the Torch library, used for applications such as computer vision and natural language processing. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

ML 121
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How to Use Hugging Face Pipelines?

Towards AI

Hugging Face is a platform that provides pre-trained language models for NLP tasks such as text classification, sentiment analysis, and more. The NLP tasks we’ll cover are text classification, named entity recognition, question answering, and text generation. The pipeline we’re going to talk about now is zero-hit classification.

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Advanced RAG patterns on Amazon SageMaker

AWS Machine Learning Blog

You can deploy this solution with just a few clicks using Amazon SageMaker JumpStart , a fully managed platform that offers state-of-the-art foundation models for various use cases such as content writing, code generation, question answering, copywriting, summarization, classification, and information retrieval.

LLM 138
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Falcon 2 11B is now available on Amazon SageMaker JumpStart

AWS Machine Learning Blog

It’s a next generation model in the Falcon family—a more efficient and accessible large language model (LLM) that is trained on a 5.5 It’s built on causal decoder-only architecture, making it powerful for auto-regressive tasks. After deployment is complete, you will see that an endpoint is created.

Python 128