altolending

Not another explanation You’ve probably heard of data leakage, and you might know both flavours well: Target Variable and Train-Test Split. But will you spot the holes in my faulty logic, or the oversights in my optimistic code? Let’s find out.  I’ve seen many articles on Data Leakage, and I thought they were are all […]

Flick through any popular Macroeconomic textbook of the last 50 years and you will quickly land on a page that mentions the Phillips curve. And then another page, and then another… For example, the Macroeconomics textbook, creatively titled “Macroeconomics” by C. Jones and part of Cambridge university’s BA Economics curriculum, references the “Phillips curve” a

When I built a GPT-powered fashion assistant, I expected runway looks—not memory loss, hallucinations, or semantic déjà vu. But what unfolded became a lesson in how prompting really works—and why LLMs are more like wild animals than tools. This article builds on my previous article on TDS, where I introduced Glitter as a proof-of-concept GPT

Although normal distributions are the most commonly used, a lot of real-world data unfortunately is not normal. When faced with extremely skewed data, it’s tempting for us to utilize log transformations to normalize the distribution and stabilize the variance. I recently worked on a project analyzing the energy consumption of training AI models, using data

I really enjoyed reading this paper, not because I’ve met some of the authors before, but because it felt necessary. Most of the papers I’ve written about so far have made waves in the broader ML community, which is great. This one, though, is unapologetically African (i.e. it solves a very African problem), and I

Thank you for the kind response to Part 1, it’s been encouraging to see so many readers interested in time series forecasting. In Part 1 of this series, we broke down time series data into trend, seasonality, and noise, discussed when to use additive versus multiplicative models, and built a Seasonal Naive baseline forecast using

I recently had the chance to create an MCP server for an observability application in order to provide the AI agent with dynamic code analysis capabilities. Because of its potential to transform applications, MCP is a technology I’m even more ecstatic about than I originally was about genAI in general. I wrote more about that

It’s well known that what we eat matters — but what if when and how often we eat matters just as much? In the midst of ongoing scientific debate around the benefits of intermittent fasting, this question becomes even more intriguing. As someone passionate about machine learning and healthy living, I was inspired by a 2017 research paper[1] exploring this intersection. The

Introduction Whether you’re preparing for interviews or building Machine Learning systems at your job, model compression has become a must-have skill. In the era of LLMs, where models are getting larger and larger, the challenges around compressing these models to make them more efficient, smaller, and usable on lightweight machines have never been more relevant.

With ACP (Agent Communication Protocol), AI agents can collaborate freely across teams, frameworks, technologies, and organizations. It’s a universal protocol that transforms the fragmented landscape of today’s AI Agents into inter-connected team mates. This unlocks new levels of interoperability, reuse, and scale. As an open-source standard with open governance, ACP has just released its latest