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I currently lead a small data team at a small tech company. With everything small, we have a lot of autonomy over what, when, and how we run experiments. In this series, I’m opening the vault from our years of experimenting, each story highlighting a key concept related to experimentation. Georandomization & How we optimized

At some point or another, any Power BI developer must write complex Dax expressions to analyze data. But nobody tells you how to do it. What’s the process for doing it? What is the best way to do it, and how supportive can a development process be? These are the questions I will answer here. Introduction 

Introduction In the “ever rapidly changing landscape of Data and AI” (!), understanding data and AI architecture has never been more critical. However something many leaders overlook is the importance of data team structure. While many of you reading this probably identify as the data team, something most don’t realise is how limiting that mindset can be.

1. Introduction It’s pretty clear that most of our work will be automated by AI in the future. This will be possible because many researchers and professionals are working hard to make their work available online. These contributions not only help us understand fundamental concepts but also refine AI models, ultimately freeing up time to

Introduction: Can AI really distinguish dog breeds like human experts? One day while taking a walk, I saw a fluffy white puppy and wondered, Is that a Bichon Frise or a Maltese? No matter how closely I looked, they seemed almost identical. Huskies and Alaskan Malamutes, Shiba Inus and Akitas, I always found myself second-guessing.

Running cool experiments is easily one of my favorite parts of working in data science. Most experiments don’t deliver big wins, so the winners make for fun stories. We’ve had a few of these at IntelyCare, and I’m sharing each story in a way that highlights a concept related to experimentation. Georandomization & How we

Running experiments is a task that often falls to data scientists. If that’s you, congrats! It can be a rewarding and high-impact area of work, but also requires tools found outside the typical ML-heavy data science curriculum. Even with the best tools, only a small share of experiments deliver meaningful business value. I’ve been lucky

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,