Regardless of where your organization is on its data journey, Altair RapidMiner can help overcome the most challenging obstacles in your way. We offer a path to modernization for established data analytics teams as well as a path to automation for teams just getting started. We do this without requiring your organization to radically change your people, processes, computing environment, or existing data landscape, helping you achieve your data goals without changing who you are or what you have.
Whether your organization requires an end-to-end data science platform or point solutions, Altair RapidMiner enables you to deliver the right tool at the right time to your diverse teams.
To better understand your processes, customers, and products, diverse teams must collaboratively generate and share data-driven insights across the organization. Altair’s enterprise-ready data science platform is designed for many different skill sets, from data scientists and engineers to business analysts and executives, to do just that in one unified environment. Build data and machine learning pipelines with code-free to code-friendly experiences. Increase trust with interactive decision trees, model simulators, and other highly explainable features. Scale as needed from desktops to on-premises servers to secure, multi-tenant cloud implementations. Easily deploy models and share the insights where they will have the most impact.
To solve complex problems, you need to access data from many sources, including difficult, semi-structured data like PDFs, spreadsheets, and text files. This raw data is rarely clean or fully accurate, so your team needs to transform it for use in machine learning applications.
Whether data is structured or unstructured, on premises or in the cloud, Altair’s solutions can automate preparation tasks and transform your data into clean, accurate datasets in seconds – rather than hours or days. Replace mundane, repetitive, and error-prone tasks with value-add activities for greater impact on the business.
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Finding predictive signals in your data requires experimentation and iteration. You need to quickly identify the best model for your business case and data, then easily refine the model and explain it to stakeholders.
With Altair RapidMiner, you can streamline model creation – whether you’re new to data science or a seasoned pro – through automated, visual, and code-based approaches. Altair’s machine learning solutions allow you to easily train, evaluate, explain, and deploy predictive and prescriptive models using the latest techniques.
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Delays in decision-making are costly in time-critical businesses. Waiting for end-of-day reports means you’re likely to miss profitable opportunities or fail to respond to threats to smooth operations, regulatory compliance, or profitability until it’s too late.
Use Altair RapidMiner to spot anomalies, trends, and outliers in seconds with real-time data, and share results across the organization using rich, powerful dashboards. Altair’s stream processing and data visualization solutions are built for people who need to make fast, fully informed decisions based on massive amounts of fast-changing telemetry, sensor, and trading data.
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Organizations that have invested many years developing IP using SAS language have options, whether you want to transition to open-source languages, choose the infrastructure that works best for your organization, or save on software cost.
Altair has the solutions to maintain and run your existing SAS language programs without the need for any third-party products. Write data-centric applications using the best programming languages for the job, mixing syntax from different languages inside a single program. Choose cloud, on-premises, or hybrid infrastructure.
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The Internet of Things (IoT) has empowered organizations to become truly more data-driven and automate business processes that depend on data. IoT devices combined with data science tools help analytics teams and stakeholders improve efficiency, deliver data analytics insights, and improve decision-making processes.
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