As AI models continue to increase in scope and accuracy, even tasks once dominated by traditional algorithms are gradually being replaced by Deep Learning models. Algorithmic pipelines — workflows that take an input, process it through a series of algorithms, and produce an output — increasingly rely on one or more AI-based components. These AI…

Introduction Writing code is about solving problems, but not every problem is predictable. In the real world, your software will encounter unexpected situations: missing files, invalid user inputs, network timeouts, or even hardware failures. This is why handling errors isn’t just a nice-to-have; it’s a critical part of building robust and reliable applications for production.…

previous article on organizing for AI (link), we looked at how the interplay between three key dimensions — ownership of outcomes, outsourcing of staff, and the geographical proximity of team members — can yield a variety of organizational archetypes for implementing strategic AI initiatives, each implying a different twist to the product operating model. Now…

As we have already seen with the basic components (Part 1, Part 2), the Hadoop ecosystem is constantly evolving and being optimized for new applications. As a result, various tools and technologies have developed over time that make Hadoop more powerful and even more widely applicable. As a result, it goes beyond the pure HDFS…

DBeaver is the most powerful open-source SQL IDE, but there are several features people don’t know about. In this post, I will share with you several features to speed up your workflow, with zero fluff. I’ve learned these as I’m currently digging deeper into the tools I use daily, starting with Dbeaver. In a future…

What if you want to write the whole object detection training pipeline from scratch, so you can understand each step and be able to customize it? That’s what I set out to do. I examined several well-known object detection pipelines and designed one that best suits my needs and tasks. Thanks to Ultralytics, YOLOx, DAMO-YOLO,…

1. Introduction Ever since the introduction of the self-attention mechanism, Transformers have been the top choice when it comes to Natural Language Processing (NLP) tasks. Self-attention-based models are highly parallelizable and require substantially fewer parameters, making them much more computationally efficient, less prone to overfitting, and easier to fine-tune for domain-specific tasks [1]. Furthermore, the…

Imagine you’re building your dream home. Just about everything is ready. All that’s left to do is pick out a front door. Since the neighborhood has a low crime rate, you decide you want a door with a standard lock — nothing too fancy, but probably enough to deter 99.9% of would-be burglars. Unfortunately, the local homeowners’…

Previously we discussed applying reinforcement learning to Ordinary Differential Equations (ODEs) by integrating ODEs within gymnasium. ODEs are a powerful tool that can describe a wide range of systems but are limited to a single variable. Partial Differential Equations (PDEs) are differential equations involving derivatives of multiple variables that can cover a far broader range…

Machine learning and AI are among the most popular topics nowadays, especially within the tech space. I am fortunate enough to work and develop with these technologies every day as a machine learning engineer! In this article, I will walk you through my journey to becoming a machine learning engineer, shedding some light and advice…