Machine Learning builds models automatically for instant, accurate predictions.

SquarkTM is a Software as a Service (SaaS) platform that applies to any classification problem in any industry, enabling business users to create highly accurate predictions—in minutes. Now, the brilliance of data science is packaged in an easy, online AutoML app. You don’t need to learn the complexities Artificial Intelligence or hire data scientists and programmers to get

Squark Seer operates as simply as a spreadsheet. You don’t write a single character of code or script. Using supervised machine learning, Seer builds and compares scores of models automatically from your training data and chooses the best one. Remarkably, the accuracy of Seer models is equal to or better than with custom development projects.

This is the productivity breakthrough that puts AI power into daily use. Finally, you see timely answers to predictive questions—insights that were impossible using reports and traditional business information tools.

As data changes, simply re-run models to update them. Since Squark does not have per-use fees, you can build and run any number of models as often as you like.

  • Fastest ROI
  • Easy for Business Users and Analysts
  • Load Data from Any Source
  • Scalable to Any Size Team
  • Models Using Latest and Best Data Science AI Algorithms
  • No Coding, No Scripting, No Programming of Any Kind
  • No Software Installation or Maintenance
  • Can Be Integrated with Other Systems

Simply upload your training data and click “next” to launch AutoML – which means Seer does all the heavy lifting automatically using machine learning. Seer’s data intelligence suggests which prediction variables to use and allows you accept or override. Multiple model types—from deep learners to gradient boosters to decision trees to ensemble models and more—are built automatically, thousands of times. Those models are tested, validated, and ranked to select the one that performs best.

Seer also explains which variables are most important to the predictive results. After all, understanding why the model works is critical to knowing how it will perform with real-world information. The final step is to run production data. Even large data sets with many variables are processed quickly, showing predictions in the same, interactive session. Start to finish is measured in minutes.