If you’re subscribed to firstname.lastname@example.org, or work in a large company, you may see some common Spark error messages. Even attending Spark Summit over the past few years you have seen talks like the “Top K Mistakes in Spark.” While cool non-machine learning based tools do exist to examine Spark’s logs — they don’t use machine learning and therefore are not as cool but also limited in by the amount of effort humans can put into writing rules for them. This talk will look what happens when we train “regular” clustering models on stack traces, and explore DL models for classifying user message to the Spark list. Come for the reassurance that the robots are not yet able to fix themselves, and stay to learn how to work better with the help of our robot friends. The tl;dr of this talk is Spark ML on Spark output, plus a little bit of Tensorflow is fun for the whole family, but probably shouldn’t automatically respond to user list posts just yet.