Skip to main content
African American woman sitting at a desk using Altair RapidMiner Cloud software.

人工智慧 (AI) 與資料分析解決方案

資料不同於企業擁有的任何其他資產。 資料永不磨損,永不流失,可以重複使用。 但資料的價值不在於擁有它,而是如何使用它。 Altair 可為資料驅動的企業提供使用 AI 和資料分析解決方案的能力,從而獲得競爭優勢並推動實現更高層級的業務成果,最終實現資料驅動型企業。

瀏覽產品

讓每個人具備培養資料驅動文化的能力

借助 Altair 的資料分析解決方案,您可以擴展 AI 計劃,而無需龐大的資料科學家團隊或昂貴的服務。 提高員工技能,使從新手到專家級別的用戶都可以試用所需的資料和分析工具來提供數據驅動的見解。

業務團隊與分析師

無需編寫任何程式碼即可在整個組織內產生和共享資料驅動的洞察。

資料科學家

專注於高價值的工作,從程式碼自由到程式碼友善的選項和協作。 輕鬆部署和監控模型,以實現長期業務影響。

資料架構師和 IT

轉變組織的資料架構並大規模管理複雜的自動化。

Altair® RapidMiner®

我們的資料分析和 AI 平台 Altair RapidMiner 提供全面的端對端解決方案,從資料擷取和建模到營運和視覺化。

深入了解

提供正確的資料和進階分析工具

為多元化團隊提供成功所需的資料和分析能力的廣度和深度。 無論是統一的端對端資料科學解決方案、自助式資料轉換或視覺化解決方案,還是替代的 SAS 語言環境。

從 PDF、電子表格和報告中提取資料是業務的核心

連接資料庫、電子表格、大數據、IoT 等

探索趨勢並發現異常

轉換資料以適應您的應用

訓練和評估 AI 模型,從無程式碼到程式碼友好

大規模運作模型

開發即時看板或終端使用者應用程式

在雲端或邊緣實現自動化,增強流程功能

控制終端使用者對資料的存取

Altair RapidMiner 在整個分析生命週期中提供廣度和深度。

深入了解

克服企業級挑戰

不要讓 IT 挑戰妨礙您的資料分析解決方案。 了解您所需的可擴展性和部署選項 - 所有這些都不會影響資料的安全性或完整性。

確保安全與管理

透過詳細的存取控制來強制實行監督。 輕鬆與現有的企業用戶管理系統整合。

隨時部署

靈活的部署模式包括託管的本機、雲端、或混合式解決方案。

現在與未來的補充工具

發展您的分析生態系統。 將當前的投資與未來的願景結合。

透過 AI 加速整個企業創新

解決高影響力的 AI 案例,改變您的業務。 透過增強每個人的能力和提供正確的工具,您可以使用資料和進階分析工具實現無限的目標。

推動營收成長

  • 需求預測
  • 文字挖掘
  • 客戶終身價值
  • 下一個最佳行動
  • 客戶細分
  • 向上銷售和交叉銷售

削減成本

  • 預測性維護
  • 供應鏈優化
  • 流程自動化
  • 產品開發
  • 防止客戶流失
  • 自動化資料擷取

管理風險

  • 信用記分卡 
  • 品質保證
  • 保固分析
  • 避災 
  • 法規遵從性
  • 詐欺偵測
  • 網路安全  
  • 貿易監測

透過流暢 AI,加速企業人工智慧 (AI) 應用。

深入了解

特色資源

50 Ways to Impact Your Business with AI

Identifying potentially impactful use cases is one of the most cited roadblocks for organizations seeking to leverage AI in their business. To complicate things further, best practices dictate that you should have a portfolio of use cases ready to experiment with. If finding one is a challenge, developing a whole portfolio of use cases may prove to be very difficult.

In this guide, we'll cover:

  • A wide variety of AI applications for enterprises
  • The challenges that led each business to seek help from AI & machine learning
  • The advanced solutions that were built and deployed to overcome each challenge
  • The documented financial impact experienced by each client
型錄

A Leader's Guide to Building a Data-Driven Culture

If you have mountains of data at your fingertips that you're not using, you risk falling behind your competition. But, if you actively work toward becoming a more data-driven organization and closing the pervasive data science skills gap, you can promote internal alignment around how data is used, make a tangible impact with AI, and come out on top. The best time to start optimizing how data is viewed and used at your organization is right now, and in this whitepaper, we're going to walk you through how to do just that.

白皮書

Guide to Using Data Analytics to Prevent Financial Fraud

Financial fraud takes countless forms and involves many different aspects of business including; insurance and government benefit claims, retail returns, credit card purchases, under and misreporting of tax information, and mortgage and consumer loan applications.

Combating fraud requires technologies and business processes that are flexible in their construct, can be understood by all who are involved in fraud prevention, and are agile enough to adapt to new attacks without needing to be rebuilt from scratch. Armed with advanced data analytics, firms and government agencies can identify the subtle sequences and associations in massive amounts of data to identify trends, patterns, anomalies, and exceptions within financial transaction data. Specialists can use this insight to concentrate their attention on the cases that are most likely fraud.

This guide will help you understand the complex environment of financial fraud and how to identify and combat it effectively.

線上指南

Make Machine Learning Work for You

Protecting consumers and enterprises involved in online transactions is just one example of how machine learning (ML) influences our daily lives. In fact, the list of use cases is already long, diverse and growing fast. The reason is clear – ML is a game-changing tool that enables organizations to make better decisions faster. What's more, ML is highly effective at balancing conflicting objectives.

Given the breadth and depth of potential use cases, one thing is clear – more and more people will find themselves working in environments where ML plays a critical role. And thanks to the emergence of low-code and no-code software, ML is no longer the exclusive preserve of programmers, data scientists, and people who paid attention in math class. More of us can, and will, be involved in developing and deploying practical ML solutions.

This eGuide will help you understand the key concepts behind ML, some common applications, and how ML becoming more useful to people at all levels of the modern organization.

線上指南
View All Resources

保持聯絡

我們能提供哪些協助?

期待您與我們聯絡。 請透過以下方式與我們聯絡。

聯絡我們
careers-cta-pic