python

Recently I’ve found myself wanting to dig a bit deeper into PyTorch to really understand how it works and hopefully figure out how to expand on my skillset to try new things. To aid this, I’ve been following the Deep Learning with PyTorch book by Eli Stevens, Luca Antiga and Thomas Viehmann. The book itself has been great and highly useful. I recommend it to anyone. In my observations, one the hardest things for newer users of PyTorch to understand is how to train a model....

Recently, I’ve been involved in experiment design and measurement - specifically AB Testing. This experience has encouraged me to learn more about experimentation because of unique challenges faced with conversion optimisation. This post, goes hand in hand with lead score experimentation. In brief, the unique challenges posed were: Dealing with largely a non-technical audience Low sample sizes Costly human interventions as a variant. Because of this, I chose Bayesian AB Testing as a measurement framework....

There are a lot of articles out there explaining how to wrap Flask around your machine learning models to serve them as a RESTful API. This article assumes that you already have wrapped your model in a Flask REST API, and focuses more on getting it production ready using Docker. Motivation Why do we need to further work on our Flask API to make it deployable? Flask’s built-in server is not suitable for production Docker allows for smoother deployments, more reliability, and better developer-production parity than attempting to run Flask on a standard Virtual Machine....