數據分析與人工智能AI

數據分析和人工智能 (AI)

數據不同於您組織擁有的任何其他資產。數據永不磨損,永不流失,並且可以反復使用。但數據的價值不在於擁有它,而在於您如何使用它。Altair 為團隊提供使用數據分析和 AI 的能力來獲得競爭優勢並推動實現更高級別的業務成果,從而實現數據驅動型企業。

流覽產品

2021 Future.AI 會議演示現可按需提供

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操作數據

使用您的 Data 和 AI

為了更好地了解您的流程、客戶和產品,您的團隊必須在整個組織中協作生成和共享數據驅動的深度資訊。我們的解決方案面向具有多種不同技能的人員:從資料科學家和工程師到 MLOps 專家和業務分析師,再到高管。通過無代碼、支援雲的介面,我們提供組織所需的強大功能,以在整個資料管道中充分利用數據分析和 AI。

Altair Data Analytics 使組織能夠通過安全、受監管和可擴展的策略實施數據分析和 AI。

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人工智能驅動設計

人工智能驅動設計

AI 和機器學習 (ML) 領域的發展,加上強大的模擬、測試和現場數聚集的可用性增加,使工程數據科學成為現代產品開發生命週期的關鍵組成部分。AI 增強的電腦輔助工程 (CAE) 使製造商能夠發現 ML 引導型深度資訊,通過物理和 AI 驅動的工作流程探索複雜設計問題的新解決方案,並通過協作和設計融合實現更多的產品創新。

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數據轉換

數據轉換

作為 30 多年的數據發現和轉型的行業領導者,Altair 提供從半結構化數據(如 PDF 和文字檔)及大資料和其他結構化數據來源提取數據的最快、最簡單的解決方案。無論數據是在本地還是在雲端,Altair 都可以自動執行準備任務,並在幾秒鐘內(而不是幾小時或幾天)將您的數據轉換為準確、乾淨的數據集,讓您騰出時間花在增值活動上,而不是用於平凡、重複且容易出錯的任務。

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預測分析和機器學習

預測分析和機器學習

Altair 的機器學習和 AI 解決方案可快速獲取細細微性、低延遲數據,其中包含您想要的深度資訊。通過 AutoML 和可解釋 AI 等功能提供透明度和自動化,我們簡化了模型構建,因此可以有更多時間進行分析並且可以信任結果。我們靈活的無代碼方法不限制模型的配置和調整方式,讓您可以控制模型構建。借助我們對流行的開來源語言和引擎的支援,您可以將使用 Altair 構建的新模型集成到您現有的分析基礎架構中。

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實時操作性能

數據可視化和流程處理

在幾秒鐘內即可發現數據異常、趨勢和離群值。利用豐富、強大的儀錶板在整個組織內共用結果。我們的流處理和數據可社化解決方案適用于需要根據大量快速變化的遙測、感測器和交易數據快速做出明智決策的人員。

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2021 FROST & SULLIVAN

科技進步領袖獎

Altair 憑藉卓越製造的數據和人工智能解決方案榮獲 2021 年 Frost & Sullivan 北美科技進步領袖獎。

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特色資源

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.

eGuide

Harnessing the Power of Big Data, AI and Simulation to Accelerate Product Innovation

In a world where everything is becoming more and more connected, Mabe, a leader in home appliances, is leveraging the convergence of big data, analytics and simulation to accelerate innovation. Martin Ortega, Senior Design Engineer at Mabe, explains how they are using Altair’s AI, data analytics and simulation solutions to uncover insights, create new business opportunities, and advance product development. Learn more - click here to read how connected products deliver big ROI.

Testimonial

Visualize Power Flows in Real Time

The Electric Storage Company is a Northern Ireland-based firm that manages electric power in households from renewable sources using battery storage and Internet of Things (IoT) technologies. The company installs smart batteries in homes and communities, along with sophisticated management software that lets homeowners sell excess energy back to grid operators when prices are high and helps them maintain the lowest possible energy input costs. Managing varieties of base load and intermittent renewable power sources requires the ability to ingest, process, and analyze high frequency information emanating from the grid and thousands of devices. The company needs real-time insight into energy markets, the grid, battery systems, and generation facilities, as well as customer-level power consumption patterns. Understanding consumption and generation trends optimizes power routing and battery storage and ensures that power sold back to the grid or on the open market is fetching the best possible price.

Customer Stories

Guide to Using Altair Data Analytics to Estimate and Visualize Electric Vehicle Adoption

Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair’s core differentiators. While Altair’s data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they’re also useful in a variety of more common business intelligence applications, too. Explore how machine learning drives EV adoption insights - click here. An Altair team undertook a project utilizing Altair Knowledge Studio® machine learning (ML) software and Altair Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level. This guide explains the team’s findings and the process they used to arrive at their conclusions.

eGuide
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