Remove APIs Remove Metrics Remove Scripts
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Integrate dynamic web content in your generative AI application using a web search API and Amazon Bedrock Agents

AWS Machine Learning

Amazon Bedrock agents use LLMs to break down tasks, interact dynamically with users, run actions through API calls, and augment knowledge using Amazon Bedrock Knowledge Bases. In this post, we demonstrate how to use Amazon Bedrock Agents with a web search API to integrate dynamic web content in your generative AI application.

APIs 103
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How to decide between Amazon Rekognition image and video API for video moderation

AWS Machine Learning

Amazon Rekognition has two sets of APIs that help you moderate images or videos to keep digital communities safe and engaged. Some customers have asked if they could use this approach to moderate videos by sampling image frames and sending them to the Amazon Rekognition image moderation API.

APIs 73
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How Druva used Amazon Bedrock to address foundation model complexity when building Dru, Druva’s backup AI copilot

AWS Machine Learning

Dru on the backend decodes log data, deciphers error codes, and invokes API calls to troubleshoot. This approach allowed us to break the problem down into multiple steps: Identify the API route. Generate and invoke private API calls. Having similar names and synonyms in API routes make this retrieval problem more complex.

APIs 106
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Few-shot prompt engineering and fine-tuning for LLMs in Amazon Bedrock

AWS Machine Learning

Investors and analysts closely watch key metrics like revenue growth, earnings per share, margins, cash flow, and projections to assess performance against peers and industry trends. Traditionally, earnings call scripts have followed similar templates, making it a repeatable task to generate them from scratch each time.

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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning

Customers can use the SageMaker Studio UI or APIs to specify the SageMaker Model Registry model to be shared and grant access to specific AWS accounts or to everyone in the organization. We will start by using the SageMaker Studio UI and then by using APIs.

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Amazon Bedrock Custom Model Import now generally available

AWS Machine Learning

This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,

APIs 136
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Build an air quality anomaly detector using Amazon Lookout for Metrics

AWS Machine Learning

This post shows you how to use an integrated solution with Amazon Lookout for Metrics and Amazon Kinesis Data Firehose to break these barriers by quickly and easily ingesting streaming data, and subsequently detecting anomalies in the key performance indicators of your interest. You don’t need ML experience to use Lookout for Metrics.

Metrics 77