There are a number of patterns for using Machine Learning (ML) models in a production environment, such as offline, real-time, and streaming. In this article, we will take a look in detail at how to use ML models for online prediction.
You can find a dozen articles on “How to build REST API for ML”. The problem is that almost all of them describe this topic very superficially. …
“All models are wrong, but some are useful.” — George Box.
To build a solution using Machine Learning (ML) is a complex task by itself. Whilst academic ML has its roots in research from the 1980s, the practical implementation of Machine Learning Systems in production is still relatively new.
There are hundreds of restaurants in each city, thousands of movies and millions of other high-quality products for which personalized recommendations allow us to save a lot of time. Recommendation systems (RS) have become a ubiquitous service in our time.
In this article, we will consider how to build a recommendation system using Bayesian Personalized Ranking with a focus on practical application. However, we will start with a short theoretical introduction for a better understanding of what is going on under the hood.
There are three…
Machine Learning Engineer at N26 | Researcher, Software Engineer, open-source enthusiast | Background in physics & engineering