There are quite a few machine learning API services. Here are a few:

* BigML https://bigml.com/

* Dato https://dato.com/

* Amazon Machine Learning http://aws.amazon.com/machine-learning/

* Google Prediction API https://cloud.google.com/prediction/docs

* Azure Machine Learning http://azure.microsoft.com/en-us/services/machine-learning/

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.

Thanks for sharing! Knew of Google, Amazon and Azure solutions. BigML and Dato looks interesting!

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.

Also built-in support for other data store backends e.g., PostgreSQL, MySQL, ...see https://docs.prediction.io/system/anotherdatastore/ since 0.9.3 release.

Storage is not what engineers struggle with its the ML setup.