AWS 機器學習能力合作夥伴

透過擁有深厚技術知識和經客戶成功實證的 AWS 專業化合作夥伴,推動創新並發揮更高的商業價值

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。

AWS 機器學習能力合作夥伴標誌

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。

在整個資料生命週期中針對機器學習模型提供持續整合與持續部署解決方案,包括建立資料湖、各資料服務的自動化資料預先處理、在雲端部署,以及機器學習特定的規則和程序,以進行模型重新部署。

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與經過技術驗證的 AWS 合作夥伴合作,推動創新、達成業務目標,並充分發揮 AWS 服務效益。

AWS 合作夥伴能源能力標誌

其他資源

探索更多的 AWS 機器學習能力合作夥伴解決方案和資源。

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  • 將您的機器學習投資最大化

    探索 AWS 機器學習能力合作夥伴電子書、網路研討會、客戶成功案例等。

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    機器學習基礎知識

    觀賞隨需網路研討會,深入了解這些技術對於數位轉型的重要性。

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    • Financial Services

      Paynela Improves Data Accessibility Using generative AI With the Help of Mission

      United States

      Paynela, a Puerto Rico-based healthcare financing innovator, revolutionized patient financial assistance through cutting-edge solutions powered by AWS Partner Mission. Dedicated to making healthcare more accessible, Paynela helps patients manage their out-of-pocket medical expenses with dignity and ease. By leveraging Amazon Web Services (AWS) and Mission's expertise, Paynela harnessed the power of generative AI to analyze complex healthcare data, resulting in deeper insights and expanded patient support capabilities.

      2025
    • Software & Internet

      NeuralSpace Accelerates AI Model Training Speed by 96% in Migration to AWS with Rebura

      United Kingdom

      NeuralSpace, a London-based AI startup, had the same problem that many startups have: not enough time, not enough money, and too much to do. It needed to develop and train the AI models that powered its language AI applications—automatic translation of text and speech, automated subtitling, and automated AI dubbing of content—but these processes were taking too long. With 20–30 TB of data being used to train each model, it could take 3–6 months to train just one. And the company needed to train multiple models to develop its products. NeuralSpace knew that it needed to find a way to speed up model training that would fit within its limited budget. With the help of AWS Partner Rebura, NeuralSpace migrated to Amazon Web Services (AWS) to enable faster modeling and a crucial pivot in focus.

      2024
    • Manufacturing

      Usiminas, with Enkel’s Support, Reduces R$ 9 Million in Transportation Costs by Optimizing Processes Through AWS Services

      Brazil

      Usiminas, supported by AWS Partner Enkel tackled significant challenges in freight pricing and route optimization within the steel industry. By developing a customized automation platform based on AWS services, the Brazilian steel company streamlined logistics processes, leading to a 14% reduction in costs and significant efficiency gains. The project not only cut delivery distances by 448,000 kilometers but also resulted in a notable decrease in CO2 emissions, avoiding the release of 1,577 metric tons. This project enhances Usiminas' market competitiveness and reinforces its commitment to environmental sustainability, demonstrating the positive impact of innovative solutions in the logistics sector.

      2024
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  • 電子書
  • 金融服務 AI 解決方案

    Appen 的人工智慧 (AI) 專家解釋如何識別和實作成功的機器學習及 AI 計畫。

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    機器學習觸手可及

    了解如何連線至 Amazon SageMaker 以大規模開發、測試和部署機器模型,並利用符合成本效益的依用量計費定價。

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    將 AI 融入貴企業

    了解如何為 AI 做好準備、嵌入 AI 並快速將其運用到生產中,以解決複雜的業務問題。

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    挖掘您的資料湖以取得分析洞察

    了解如何在 Databricks 和 AWS 上使用 Delta Lake 來準備並交付能夠用於分析以取得洞察的資料。

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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.
    • 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.
    • 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.
    • 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.
    • 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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