Deploy it in less than 5 minutes with Terraform

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While working with complex and multi-module Python projects it quickly becomes crucial to share libraries across different components, enable developers to easily install those libraries into their local development environment, and use them in continuous integration tools. A private PyPi repository is a good solution to this problem since it allows installing internal libraries anywhere just by using regular pip install commands while keeping full control over the Python packages.

If your application is running on the cloud, you likely want to deploy your PyPi server within your infrastructure. In this post, I focus on the AWS cloud and show…


A step-by-step guide to not reinvent the wheel

Photo by Seyi Ariyo on Unsplash

Introduction

At its core, cloud computing is just about running an application on third-party hardware. However, over the last decade, modern cloud applications required increasingly complex infrastructure in order to run securely, guarantee high availability, and scale smoothly when the number of users increases.

Let us take the case of AWS. The number of infrastructure resources to provision for a simple (single-tenant) application goes easily above 50, not considering the need for different environments such as staging, development, and production. This is simply impossible to maintain using the AWS web interface and it is where Infrastructure-as-a-Code (IaaC) software comes to the…


Parsing a simple JSON representation with the anytree library

Photo by Edvard Alexander Rølvaag on Unsplash

In computer science, it is very common to deal with hierarchical categorical data. Applications range from categories of Wikipedia to the hierarchical structure of the data generated by clustering algorithms such as HDBSCAN, and countless more.

For this post, let us start from an example drawn from my field of work: how to correctly classify devices connected to a network. Starting from the general concept of “Device”, one can define two general categories of devices called “Networking” and “Computer”. The first category can then be further broken down into “Router”, “Switch” and “Firewall” (and of course several others which I…


End-to-end guide to predicting the future with machine learning

Written by Mario Dagrada & Lorenzo Ghiringhello.

Photo by Aron Visuals on Unsplash

Introduction

The application of machine learning (ML) techniques to time series forecasting is not straightforward. One of the main challenges is to use the ML model for actually predicting the future in what is commonly referred to as forecasting. Without forecasting, time series analysis becomes irrelevant.

This issue stems from the temporal structure of the data since, at variance with standard ML projects, it is not enough to apply a pre-trained model on new data points to get the forecasts but, as we will see in this post, additional steps are required.

Mario Dagrada

VP of Quantum Software @ Qu&Co

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