Squark Automated Machine Learning (AutoML) Analytics

Squark’s Codeless Machine Learning Builds Instant, Accurate Prediction Models

Squark® applies machine learning automatically for any classification or regression problem. Create highly  accurate predictions—in minutes—with no programming.

Now, the brilliance of data science is packaged in an easy, online app. You don’t need to learn the complexities of Artificial Intelligence algorithms or hire data scientists and programmers to implement machine learning.

Squark operates as simply as a spreadsheet. Using supervised machine learning, Squark builds and compares scores of models automatically from your training data and chooses the best one. Remarkably, the accuracy of Squark 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 intelligence 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 Use 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.” Squark does all the machine learning heavy lifting—automatically.

Squark’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.

Squark 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.