数据分析与人工智能

数据分析和人工智能 (AI)

数据不同于您的企业组织所拥有的任何其他资产。数据永不磨损,永不流失,并且可以反复使用。但数据的价值不在于拥有它,而在于如何使用它。Altair 可为数据驱动的企业提供使用数据分析和 AI 的能力,从而获得竞争优势并推动实现更高等的业务成果,然后实现数据驱动型企业。

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2021 Future.AI 会议演示现可按需点播

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操作数据

操作您的数据和 AI

为了更好地了解您的流程、客户和产品,您的团队必须在整个组织中协作生成和共享基于数据驱动的洞察。我们的解决方案面向多种不同技能的人员:无论是数据科学家和工程师,还是 MLOps 专业人士和业务分析师,甚至高管,都能使用。通过无代码、支持云的界面,我们为企业组织提供其所需的强大功能,从而针对各种数据来源充分利用数据分析和 AI。

Altair Data Analytics 使企业组织能够通过安全、受监管且可扩展的策略实施数据分析和 AI。

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人工智能驱动的设计

人工智能驱动的设计

AI 和机器学习 (ML) 领域的发展,加上强大的仿真、测试和现场数据集的可用性增加,使工程数据科学成为现代产品开发生命周期的关键组成部分。AI 增强的计算机辅助工程 (CAE) 使制造商能够发现机器学习引导的洞察力,通过物理和 AI 驱动的工作流程探索复杂设计问题的新解决方案,并通过协作和设计融合实现更多的产品创新。

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数据转换

数据转换

作为在数据发现和转换领域拥有30多年经验的行业公司,Altair 提供从难以处理的半结构化数据(如 PDF、电子表格和文本文件)及大数据和其他结构化数据源提取数据的解决方案。无论数据是在本地还是在云端,Altair 都可以自动执行数据准备任务,并在几秒钟内(而不是几小时或几天)将您的数据转换为准确、干净的数据集,让您将时间用在增值活动上,而不是用于平凡、重复且容易出错的任务。

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预测分析和机器学习

预测分析和机器学习

Altair 的机器学习和 AI 解决方案可快速获取细颗粒度、低延迟数据,其中包含您想要的洞察。通过自动机器学习和可解释 AI 等功能提供透明度和自动化,我们简化了模型构建,因此可让用户将更多时间用于分析且结果十分可信。我们灵活的无代码方法不限制模型的配置和调优方式,让您可以控制模型构建。我们还支持主流的开源语言和引擎,因此您可以将使用 Altair 构建的新模型集成到您现有的分析基础架构中。

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实时操作性能

数据可视化和流处理

使用实时数据在几秒钟内即可发现数据异常、趋势和离群值。并使用丰富、强大的仪表板在整个组织内共享结果。我们的流处理和数据可视化解决方案专为需要根据大量快速变化的遥测、传感器和交易数据快速做出快速、充分知情决策的的人员而构建。

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ALTAIR + RAPIDMINER

Forward. Faster.

Two Innovators coming together to transform data analytics.

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特色资源

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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