This post and the code here are part of a larger repo called RecoTour, where I normally explore and implement some recommendation algorithms that I consider interesting and/or useful (see RecoTour and RecoTourII). In every directory, I have included a README file and a series of explanatory notebooks that I…

This post and the code here are part of a larger repo that I have (very creatively) called “NLP-stuff”. As the name indicates, I include in that repo projects that I do and/or ideas that I have — as long as there is code associated with those ideas — that…

The Focal loss (hereafter FL) was introduced by Tsung-Yi Lin et al., in their 2018 paper “Focal Loss for Dense Object Detection”[1]. It is designed to address scenarios with extreme imbalanced classes, such as one-stage object detection where the imbalance between foreground and background classes can be, for example, 1:1000. …

In our previous post we showed how one could use the Apache Kafka’s Python API (Kafka-Python) to productionise an algorithm in real time. In this post we will focus more on the ML aspects, more specifically on how to log information during the (re)training process and monitor the results from…

This is the first of 2 posts where we will illustrate how one could use a series of tools (mostly Kafka and MLFlow) to help productionising ML. To that aim we will set a simple scenario that we hope resembles some real-word use-cases, and then describe a potential solution. …