Machine learning at your fingertips

Apply machine learning to all your data sources

Traditionally, data scientists have used standalone software tools to apply machine learning algorithms to their datasets. Now with Pyramid, they can apply machine learning techniques to their data in the same environment that other analysts and end users are using. When analysis is conducted in the same analytic platform, there’s more opportunity for collaboration—and insight—across the organization.

INCORPORATE MACHINE LEARNING TECHNOLOGIES INTO EVERYDAY WORKFLOWS

Operationalize machine learning models

The game has changed. Organizations intuitively understand the need to incorporate machine learning technologies to gain deeper insight into their data. But their aspirations are often limited by the level of skill required to bring advanced data scientist toolsets into everyday applications.

To facilitate the AI framework, Pyramid comes with tools to deliver machine learning in R, Python, Java, JavaScript and Ruby (with more to be added in the future). And to further accelerate the uptake, the ML scripts can be shared like all other business logic elements of the Analytics OS. Pyramid also provides a machine learning marketplace to allow users to download common script elements from the public domain, or add their own scripts. The marketplace will continue to grow as new scripts are added.

PREPARE DATA FOR ADVANCED DATA SCIENCE PROJECTS

Prepare data for advanced analysis

Legacy BI tools weren’t designed with data scientists in mind. They often struggle to apply deep domain skill with advanced analytic and statistical techniques to real-world business situations. They spend an inordinate amount of time using point-and-click (Excel) tools that are manual and error-prone. With Pyramid, data scientists finally have a BI application that lets them easily do what they do best: data science. This allows them to extract meaningful insights from data using data prep tools with machine learning and scripting in R and Python. And more importantly, it gives them the tools to reproduce results of scientific research, a vital aspect of any advanced analysis.

FIND WAYS TO INCORPORATE BIG DATA SOURCES

Model data against open source technologies

Big Data is here, now. Yet invaluable data goes unmined because of technological constraints. Organizations may have the data—and the ability to access the data—but they can’t explore it in an efficient, meaningful way.

New open source technologies let organizations tap their Big Data sources. With Pyramid, data scientists can take it further: they can harness the raw computing power of these new high volumes so that their data models are streamlined and efficient.

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Additional Resources

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