For example, say I want to train two Linear Regressions and two GBD Tree Regressions.
Using different threads, Spark allows you to submit jobs at the same time (see: http://spark.apache.org/docs/latest/job-scheduling.html). If I schedule two or more training jobs and they are running at the same time: 1) Is there any risk that static worker variables or worker state could become corrupted leading to incorrect calculations? 2) Is Spark ML designed for running two or more training jobs at the same time? Is this something the architects consider during implementation? Thanks, Brandon
