AWS 機器學習能力合作夥伴已展現在 AWS 雲端提供機器學習 (ML) 解決方案的專業知識。這些合作夥伴提供一系列服務和技術,協助您為企業建立各種智慧解決方案,從支援資料科學工作流程到利用機器智慧強化應用程式都包含在內。
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依類別搜尋 AWS 機器學習能力合作夥伴
諸如擷取、合併、移除重複記錄、缺少值的補充、值的縮放/規範化、消除相關特徵、特徵工程等資料處理。
用於機器學習的無程式碼和低程式碼平台,通常具有主要的視覺化界面,可實現端到端模型開發。
高程式碼解決方案、RESTful API、GraphQL 和演算法,可讓您存取用於訓練模型的已受訓模型和元件。
AWS ML 能力合作夥伴在協助組織解決 AI 中最具挑戰性的問題方面展現出專業知識,包括資料工程、資料科學、機器和深度學習,以及大規模推理的生產部署。
開發、部署和維護機器學習應用程式,這些應用程式會對客戶業務成果產生正面影響,並增加 AWS 服務 (特別是 AWS AI Services) 的價值,以解決特定客戶的需求。
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Josh Hart, Nihilson Gnanadason, 2025年2月20日In this post, we explore the process of building a multi-tenant generative AI application using Aurora PostgreSQL-Compatible for vector storage. In Part 1 (this post), we present a self-managed approach to building the vector search with Aurora. In Part 2, we present a fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data sources, the Aurora vector store, and your generative AI application.
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Josh Hart, Nihilson Gnanadason, 2025年2月20日In this post, we discuss the fully managed approach using Amazon Bedrock Knowledge Bases to simplify the integration of the data source with your generative AI application using Aurora. Amazon Bedrock is a fully managed service that makes foundation models (FMs) from leading AI startups and Amazon available through an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case.
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Sajin Jacob, Jerry Chen, Siddarth Mohanram, Luis Barbier, Kristen Chenowith and Michelle Stahl, Arun Pradeep Selvaraj, Ryan Doty, 2025年2月20日Verisk’s Premium Audit Advisory Service is the leading source of technical information and training for premium auditors and underwriters. In this post, we describe the development of the customer support process in PAAS, incorporating generative AI, the data, the architecture, and the evaluation of the results. Conversational AI assistants are rapidly transforming customer and employee support.
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Santosh Kulkarni, Joyanta Banerjee, Mallik Panchumarthy, 2025年2月20日In this post, we explore how you can use LLMs with advanced Retrieval Augmented Generation (RAG) to generate high-quality synthetic data for a finance domain use case. You can use the same technique for synthetic data for other business domain use cases as well. For this post, we demonstrate how to generate counterparty risk (CR) data, which would be beneficial for over-the-counter (OTC) derivatives that are traded directly between two parties, without going through a formal exchange.
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Channy Yun (윤석찬), 2025年2月17日Join us for the AWS Developer Day on February 20! This virtual event is designed to help developers and teams incorporate cutting-edge yet responsible generative AI across their development lifecycle to accelerate innovation. In his keynote, Jeff Barr, Vice President of AWS Evangelism, shares his thoughts on the next generation of software development based on [...]
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