Ada offers advanced machine learning functionality backed by Spark ML library, which is a popular compute grid library for an efficient large-scale data processing and analysis.
Spark has been developed in Scala, and so it pairs well with functional programming. Nevertheless, Java, Python, and R adapters are available too. Besides scalability and a community support, one of the superior features compared to other compute/ML platforms, is that the libraries or class binaries that implement the tasks to be executed are streamed to compute nodes if not available locally.
From Spark, Ada integrated several classification (e.g., random forest), regression (e.g., gradient boost regression tree), and clustering routines (e.g., bisecting k-means) with a wide range of evaluation metrics (accuracy, AUROC, AUPR, R2, RNMSE, etc.).
On the top of that, ML results can be saved, queried, and visualized, and even exported to a (new) data set directly in Ada.