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.

In the first article of this series, we discussed How Much Does It Cost to Upgrade Rails based on our historical data working on over 100 upgrade projects. In this article we’ll discuss how to minimize the cost once you’ve decided to move forward on an upgrade so your team can continue to focus on valuable product feature or roadmap work.

Working with our team of experts to upgrade your Rails application allows you to stay compliant and take advantage of the benefits and security updates of a supported version, while also letting your team focus on revenue-generating initiatives. Still, it can be a significant investment, especially, as we covered in the previous post, if your project requires a high level of manual QA, for example.

There are, however, ways to reduce the time (and therefore cost) it takes to upgrade your Rails application when working with an external team. In this article, we’ll cover a few different strategies. Does it mean you can’t upgrade unless you do all of this? No, it doesn’t. Being an Agile team allows us to adapt to your process and workflow and work with any team to get the upgrade done. However, employing at least one of these strategies can make your upgrade more cost-effective.

You’ve decided you need to prioritize upgrading Rails. Maybe it’s a compliance issue, you’re running a version that has reached EOL and need to upgrade to a more current one. Maybe you want to benefit from some of the new features more recent versions provide. Maybe you’ve noticed the old Rails version is getting in the way of your team’s productivity. Or maybe it’s something else.

Whatever the motivation may be, upgrading Rails can be a significant effort. As such, one of the key things to consider is how expensive will it be to get you from your current version to your target version. Regardless of whether you plan to handle the upgrade in house or outsource, how long it’ll take and, therefore, how much it’ll cost, are the key questions to answer.

We have invested more than 30,000 hours in total in upgrading Rails applications, having completed more than 100 upgrade projects. In this article, we’ll leverage our historical data and what we learned to help you answer this question: How much will this cost?

We recently collaborated with Procore on a Rails upgrade for their Rails application which allows teams in the construction industry to connect their entire business process.

We spoke with Andy Maltun, Procore’s VP of Software Engineering, about the work performed to help them upgrade their app to version 5.1. According to Maltun, Procore’s R&D department is large and complex. While Procore previously had handled their Rails upgrades internally, with so many teams working on the application, it caused a lot of disruption as each team would handle only part of the upgrade. Therefore, Maltun “wanted to take a different approach this time in an effort to centralize the update and minimize the project management overhead and disruption of teams.”

Technology evolves quickly. When you first created your Rails application, it’s likely you did it using the latest version of Rails. As time passes, however, new Rails versions come out and your application becomes more and more out of touch with the latest version. But why is this important? Why does it matter?

There are several different reasons to upgrade your Rails application. In this article, I’ll list what we consider to be the most important ones.

We recently collaborated with Amitree on a Rails upgrade for their application which “is an AI assistant for email, integrated deeply within Google and Microsoft’s products and is in heavy use by real estate professionals” and were able to speak to them about their experience with OmbuLabs.