As a fan of both the ML language and Machine Learning, I would say not unambiguously but highly likely. ML as a specific language is mostly dead, living on through its descendants like OCaml, F#, Scala, etc. For the most part, people will mention an ML descendant by name if they are talking about a programming language.
I found it very interesting tool to play around, at the same time when I was playing with it (year ago or so) it felt like black box without any visible feedback on your learning process. It's very hard to tune ML system without intermidiate learning feedback.
Whether or not any of them are better than the others - it depends on what you're trying to do. Different services have different strengths and capabilities, so whether or not any of them are suitable depends on the task you're working on.
Maybe Spark + Cassandra, but Cassandra has definitely shown to be finicky when it comes to 4+ node clusters, meaning, unless you're willing to contribute time and resources to devops and dive into Java, it's a point against getting up and running quickly.
That said, this service runs on HBASE which is great, however, queries function similarly to a mapreduce. This has proven consistently slower than SQL-like alternatives and I can think of a few use-cases where you'd definitely want that added speed.
It's really a question of "good enough" and how many components of your stack you're willing to take responsibility for, in exchange for enhanced scalability and IOP/s.
For what its worth though, I think Spark (in light of the recent commitment from IBM) is here to stay, so I'd say it's the unequivocal leader in distributed load/clustering frameworks.
