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Use RAG for drug discovery with Knowledge Bases for Amazon Bedrock

AWS Machine Learning

It allows you to seamlessly customize your RAG prompts and retrieval strategies—we provide the source attribution, and we handle memory management automatically. To enable effective retrieval from private data, a common practice is to first split these documents into manageable chunks. Choose Next. Choose Next.

APIs 117
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Build a powerful question answering bot with Amazon SageMaker, Amazon OpenSearch Service, Streamlit, and LangChain

AWS Machine Learning

We implement the RAG functionality inside an AWS Lambda function with Amazon API Gateway to handle routing all requests to the Lambda. We implement a chatbot application in Streamlit which invokes the function via the API Gateway and the function does a similarity search in the OpenSearch Service index for the embeddings of user question.

APIs 76
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Incremental training with Amazon SageMaker JumpStart

AWS Machine Learning

Recently, we also announced the launch of easy-to-use JumpStart APIs as an extension of the SageMaker Python SDK, allowing you to programmatically deploy and fine-tune a vast selection of JumpStart-supported pre-trained models on your own datasets. JumpStart overview. The dataset has been downloaded from TensorFlow. Walkthrough overview.

Scripts 66
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Enhancing AWS intelligent document processing with generative AI

AWS Machine Learning

Features such as normalizing key fields and summarizing input data support faster cycles for managing document process workflows, while reducing the potential for errors. In the current scenario, you need to dedicate resources to accomplish such tasks using human review and complex scripts. This approach is tedious and expensive.

APIs 75
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Identify key insights from text documents through fine-tuning and HPO with Amazon SageMaker JumpStart

AWS Machine Learning

Fully managed NLP services have also accelerated the adoption of NLP. Amazon Comprehend is a fully managed service that enables you to build custom NLP models that are specific to your requirements, without the need for any ML expertise. This notebook demonstrates how to use the JumpStart API for text classification.

Scripts 71
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Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart

AWS Machine Learning

Note that you need to pass the Predictor class when deploying model through the Model class to be able to run inference through the SageMaker API. You can access Amazon Comprehend document analysis capabilities using the Amazon Comprehend console or using the Amazon Comprehend APIs.

APIs 92
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Build production-ready generative AI applications for enterprise search using Haystack pipelines and Amazon SageMaker JumpStart with LLMs

AWS Machine Learning

Enterprise search is a critical component of organizational efficiency through document digitization and knowledge management. You can serialize pipelines to YAML files , expose them via a REST API , and scale them flexibly with your workloads, making it easy to move your application from a prototype stage to production.