What is SystemML

Apache SystemML provides an optimal workplace for machine learning using big data. Apache SystemML can be run on top of Apache Spark, where it automatically scales your data, line by line, determining whether your code should be run on the driver or an Apache Spark cluster. Users no longer need to learn about complicated code or scaling. Apache SystemML also understands that the user's priority lies with math and algorithms. That's why Apache SystemML runs in R and Python-like syntax, allowing the user to focus on machine learning, rather than the engineering behind it.
Watch Apache SystemML in action

What is Apache SystemML?


SystemML Use Cases


SystemML was used for big data needs in an automotive use case focusing on customer satisfaction where 2 million cars, 10 million repair cases and 25 million part exchanges were considered, using sequence mining, logistic regression and intermediate result sequences.

SystemML for the automitve industry

Airport Traffic

With airports getting more and more congested, researchers used SystemML to predict passenger volumes at various airport locations. With a very large data set, researchers were able to create multiple models per location and per passenger time, while developing a time-series analysis on the change of seasons.

SystemML improves Airport Traffic

Social Banking

Researchers decided to look at social media data linked to bank data in order to identify customer segments of interest, find predictors of what people would want to purchase and gauge sentiment towards bank products. Using SystemML, they were able to do this using bivariate odds ratios and binomial proportions with confidence intervals.

SystemML for Social Banking

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Ways to Use SystemML

Get Involved

To contribute code to SystemML, you can contact us directly on Github or dev@systemml.incubator.apache.org