So what's the benefit to using SystemML on top of Spark (or another scheduler) vs. using Spark's own MLlib? From the links below, it seems MLlib supports a superset of SystemML's algorithms (with the small exception of survival analysis). Are there any plans for the projects to use insights from each other, or to merge in some way? Because it seems quite silly for Apache to sponsor two competing machine learning libraries with exactly the same goals.
https://sparktc.github.io/systemml/algorithms-reference.html
https://spark.apache.org/docs/latest/mllib-guide.html