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Use AWS PrivateLink to set up private access to Amazon Bedrock

AWS Machine Learning

It allows developers to build and scale generative AI applications using FMs through an API, without managing infrastructure. Customers are building innovative generative AI applications using Amazon Bedrock APIs using their own proprietary data.

APIs 124
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How Amp on Amazon used data to increase customer engagement, Part 1: Building a data analytics platform

AWS Machine Learning

However, as a new product in a new space for Amazon, Amp needed more relevant data to inform their decision-making process. Part 1 shows how data was collected and processed using the data and analytics platform, and Part 2 shows how the data was used to create show recommendations using Amazon SageMaker , a fully managed ML service.

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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. RAG is a popular technique that combines the use of private data with large language models (LLMs).

APIs 112
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How Amp on Amazon used data to increase customer engagement, Part 2: Building a personalized show recommendation platform using Amazon SageMaker

AWS Machine Learning

Affinities are computed either implicitly from the user’s behavioral data or explicitly from topics of interest (such as pop music, baseball, or politics) as provided in their user profiles. This is Part 2 of a series on using data analytics and ML for Amp and creating a personalized show recommendation list platform.

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Use a generative AI foundation model for summarization and question answering using your own data

AWS Machine Learning

The post Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data describes how to fine-tune an LLM using your own dataset. When that job is done, you can invoke an API that summarizes the text or answers questions about it. text.strip().replace('n',

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Use Amazon SageMaker pipeline sharing to view or manage pipelines across AWS accounts

AWS Machine Learning

You can now use cross-account support for Amazon SageMaker Pipelines to share pipeline entities across AWS accounts and access shared pipelines directly through Amazon SageMaker API calls. To further monitor those workflows, data scientists now require cross-account read-only permission to the deployed pipeline in the test account.

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Identify objections in customer conversations using Amazon Comprehend to enhance customer experience without ML expertise

AWS Machine Learning

Since conversational AI has improved in recent years, many businesses have adopted cutting-edge technologies like AI-powered chatbots and AI-powered agent support to improve customer service while increasing productivity and lowering costs. accuracy by training on 800 data points and testing on 300 data points.

APIs 62