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GraphStorm 0.3: Scalable, multi-task learning on graphs with user-friendly APIs

AWS Machine Learning

adds new APIs to customize GraphStorm pipelines: you now only need 12 lines of code to implement a custom node classification training loop. Based on customer feedback for the experimental APIs we released in GraphStorm 0.2, introduces refactored graph ML pipeline APIs. Specifically, GraphStorm 0.3 In addition, GraphStorm 0.3

APIs 116
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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 105
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Build a multi-tenant generative AI environment for your enterprise on AWS

AWS Machine Learning

It also uses a number of other AWS services such as Amazon API Gateway , AWS Lambda , and Amazon SageMaker. API Gateway is serverless and hence automatically scales with traffic. API Gateway also provides a WebSocket API. As a result, building such a solution is often a significant undertaking for IT teams.

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Enhance conversational AI with advanced routing techniques with Amazon Bedrock

AWS Machine Learning

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) from leading AI companies like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon using a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.

APIs 136
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Metrics for evaluating an identity verification solution

AWS Machine Learning

Then we dive into the two key metrics used to evaluate a biometric system’s accuracy: the false match rate (also known as false acceptance rate) and false non-match rate (also known as false rejection rate). We use FMR and FNMR as our two key metrics to evaluate facial biometric systems. False non-match rate.

Metrics 97
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Considerations for addressing the core dimensions of responsible AI for Amazon Bedrock applications

AWS Machine Learning

The rapid advancement of generative AI promises transformative innovation, yet it also presents significant challenges. For automatic model evaluation jobs, you can either use built-in datasets across three predefined metrics (accuracy, robustness, toxicity) or bring your own datasets.

APIs 110
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Amazon Textract’s new Layout feature introduces efficiencies in general purpose and generative AI document processing tasks

AWS Machine Learning

Building document processing and understanding solutions for financial and research reports, medical transcriptions, contracts, media articles, and so on requires extraction of information present in titles, headers, paragraphs, and so on. Text – Text that is present typically as a part of paragraphs in documents.

APIs 115