From one hand, a machine learning model built with spark can’t be served the way you serve in Azure ML or Amazon ML in a traditional manner.
Databricks claims to be able to deploy models using it’s notebook but I haven’t actually tried that yet.
On other hand, you can use a model in three ways :
- Training on the fly inside an application then applying prediction. This can be done in a spark application or a notebook.
- Train a model and save it if it implements an
MLWriterthen load in an application or a notebook and run it against your data.
- Train a model with Spark and export it to PMML format using jpmml-spark. PMML allows for different statistical and data mining tools to speak the same language. In this way, a predictive solution can be easily moved among tools and applications without the need for custom coding. e.g from Spark ML to R.
Those are the three possible ways.
Of course, you can think of an architecture in which you have RESTful service behind which you can build using spark-jobserver per example to train and deploy but needs some development. It’s not a out-of-the-box solution.
You might also use projects like Oryx 2 to create your full lambda architecture to train, deploy and serve a model.
Unfortunately, describing each of the mentioned above solution is quite broad and doesn’t fit in the scope of SO.