Starting in Rails 7.1, Docker files are added by default in new applications, but Docker has been popular for Rails development for many years before that. At FastRuby.io, we use the Dual-Boot technique when we work on upgrades, and using that approach when an application uses Docker requires some extra steps to keep a great development experience.

Maintaining an up-to-date Ruby on Rails application is crucial for performance, security, and feature enhancements. However, finding the time for a full-scale upgrade can be challenging. In this blog post, we’ll explore practical steps you can take to delay a Rails upgrade when you don’t have the immediate bandwidth but want to set the stage for a seamless process in the future.

Welcome to the third article in our Machine Learning with Ruby series!

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. We then looked into our first ensemble model technique, Random Forests, in Machine Learning: An Introduction to Random Forests. It is a good idea to review that article before diving into this one.

Random Forests are great for a wide variety of cases, but there are also situations where they don’t perform quite as well. In this article we’ll take a look at another popular tree-based ensemble model: Gradient Boosting.

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. Decision trees are very flexible and are a good tool for simple classification, but they are often not enough when it comes to real-world scenarios.

When dealing with large and complex data, or when dealing with data with a significant amount of noise, we need something more powerful. That’s where ensemble models come into play. Ensemble models combine a number of weak learners to build a strong model, with increased accuracy and robustness. Ensembles also help manage and reduce bias and overfitting.

In this article, we’ll cover a very popular tree-based ensemble model: Random Forest.

In the middle of last year, we released an internal tool to help address a pretty significant issue. That is how the Pecas tool was born, and you can read about the Business Case for Pecas here.

Pecas relies on a binary classification machine learning model to classify time entries as valid or invalid. It is a combination of a Django app, that hosts the Slackbot and other data processing tasks, and a FastAPI app that hosts the machine learning model built using the Scikit-learn Python library. Scikit-learn provides a great set of classification models you can use, which are optimized and very robust, making it a solid choice to build your model. However, understanding the principles behind the classification can be a bit tricky, and machine learning models can feel a bit like a black box.

In this series, we’ll explore some principles of machine learning, namely binary classifiers, and walk through how they connect to each other, in Ruby. This article will focus on decision trees, namely CART (Classification And Regression Trees) and a little bit of the mathematics behind them.

When it comes to improving application performance and areas to focus on, I would recommend looking at the APM data, and then deciding which areas to prioritize.

However this article isn’t about where to focus efforts, but rather a compilation of techniques to improve your application’s performance, from tackling common problems like N+1 queries and database indexing to leveraging the jemalloc memory allocator. Let’s look at these performance-boosting strategies designed to fine-tune your application.

Every year, Ruby enthusiasts anticipate the December release of a new Ruby version. At the end of 2023, Ruby 3.3 was released and you can read this article to learn more about the new features and improvements it brings. This makes it a good time to start considering if your application is due for an upgrade.

Upgrading your application can sound complicated and difficult, but it doesn’t have to be. Below, we will discuss how to identify when to upgrade your Ruby version and the steps necessary to complete a smooth and successful upgrade.