[arauhala]

The reason, why the predictive queries create value relates to the simplified workflow and the simplified architecture.

Instead of defining model, training model and using model, you merely ask for an arbitrary unknown variable, based on any arbitrary facts. This provides much easier interface, much faster iteration cycle and other technical benefits like the ability to create generic query templates. These benefits stand even when compared to the AutoML platforms (which also do lot of heavy lifting to simplify the workflow).

Regarding the architecture and process: the system has a lot of resemblance to normal databases (and especially to the Lucene like search engines), but in order to serve arbitrary predictive queries, the entire database is specialized in-and-out for counting statistics and doing millisecond time-frame ML modeling. The things are somewhat described in the article, but I'm also happy to answer to additional questions about the system.

As interesting details the underlying database is very functional programming oriented and build on a Git-like system. We'd like to expose the database's snapshot and branching abilities in the future.

[jamez1]

So effectively, you've added a set of ML/statistics scripts to the query engine? But the query engine is otherwise still relational based?

[arauhala]

No scripts. The change is much deeper, because Aito uses ad hoc / lazy models to provide the predictive query capabilities. If you would thinly integrate some 3rd party ML library, you would end up with separate 1) model definition and 2) training steps as an addition to 3) the prediction. Aito's database is specialized for counting statistics, so that it can create ML models in millisecond scale to answer pretty arbitrary prediction queries instantly.

There is quite normal query engine working inside Aito, but the basic database query capabilities haven't been our focus right now. We have an SQL API on our roadmap, but it will likely take time, before we can even start working on it.