I strongly believe that our lives would be way easier if SQL was everything (or almost) we needed when it comes to data. (View Highlight)
The common pain in both experiences was always to find a cheap and fast way to store the data that is compatible with real-time inference and also supports experimenting, iterating, and training different models. Being a machine learning practitioner nowadays requires you to use a myriad of tools such as feature stores, training platforms, infrastructure, streams, etc, to be able to train your models and provide batches of predictions.
Now imagine a system being used by a retail company to decide which product to show you based on the probability of you buying it, while they send events to the algorithm to tell it if a shown product has been bought or not. The list of tools needed would keep getting longer. (View Highlight)
SGD is an iterative method, so having the values of the previous weights, it’s possible to generate the next ones with simple operations. Repeat the process over hundreds/thousands of events and, in theory, it will converge. (View Highlight)
First of all, let’s build a simple model using sklearn so we can use it as a reference. It will also allow us to validate results while writing our version. Since this is a classification problem, we’ll use the SGDClassifier with some special settings to simulate online gradient descent. (View Highlight)
Stochastic gradient descent with mini-batches is essentially the same but instead of going sample by sample, a batch of N samples is processed in each step. The algorithm described in pseudo-code is basically:
It’s possible to build an online machine learning algorithm by just using SQL, and thanks to ClickHouse and the Materialized Views we have managed to implement a stateful algorithm capable of predicting events in real-time. This opens the door to a lot of possibilities. (View Highlight)
We’d have to program the described algorithm in your database, build an easy way to ingest data in real-time from the online store to instantly update your model, and then provide an interface to run inference and return probabilities. (View Highlight)
