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Nowadays, data science projects don't end with the proof of concept; every project has the goal of getting used in production. It will be important, subsequently, to deliver high-quality code. I even have been...

On this fifth a part of my series, I'll outline the steps for making a Docker container for training your image classification model, evaluating performance, and preparing for deployment. AI/ML engineers would like to deal...

Introduction Data science is undoubtedly probably the most fascinating fields today. Following significant breakthroughs in machine learning a couple of decade ago, data science has surged in popularity throughout the tech community. Every year, we witness increasingly...

Through the telecommunication boom, Claude Shannon, in his seminal 1948 paper¹, posed a matter that may revolutionise technology:How can we quantify communication?Shannon’s findings remain fundamental to expressing information quantification, storage, and communication. These insights...

Raspberry Pi programmable IO pitfalls illustrated with a musical exampleThat is Part 2 of an exploration into the unexpected quirks of programming the Raspberry Pi Pico PIO with MicroPython. If you happen to missed...

The aim of this project is to learn in regards to the fundamentals of contemporary, scalable web applications by designing, constructing and deploying an AI-powered chat app from scratch. We won’t use fancy frameworks...

From Data Lakehouses to Event-Driven Architecture — Master 12 data concepts and switch them into easy projects to remain ahead in IT.Once I scroll through YouTube or LinkedIn and see topics like RAG, Agents or Quantum...

Practical insights for a data-driven approach to model optimizationOn this last a part of my series, I'll share what I even have learned on choosing a model for image classification and easy methods to...