A rating event comes into our web server (python), then sent to SQS, then the event is pulled by our custom artificial neural network (deep learning) script, --written in Go. A model is trained and serialized. Next, the serialized model is uploaded to Postgres where it is fetched by the web service (also written in Go) to serve predictions.
We update our models within 15 seconds of a user rating. Every month with millions of ratings, we re-train millions of models and serve billions of predictions.
Honestly, we haven't needed to scale horizontally. The Postgres column is a jsonb, although probably not necessary since we don't need to query the serialized model. If our reads ever start to slow down, the plan is to just add a follower to read from.
A rating event comes into our web server (python), then sent to SQS, then the event is pulled by our custom artificial neural network (deep learning) script, --written in Go. A model is trained and serialized. Next, the serialized model is uploaded to Postgres where it is fetched by the web service (also written in Go) to serve predictions.
We update our models within 15 seconds of a user rating. Every month with millions of ratings, we re-train millions of models and serve billions of predictions.