Blog
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The Future of Work With AI: Beyond Hype and Fear
AI is increasingly treated as a universal tool, with its implications on the shape of future workplaces being substantial. As labor shortage looms due to an aging population and reduced immigration, AI could amplify productivity without increasing unemployment. Professionals are actively embracing AI technologies. Research shows employers see AI as a productivity boost rather than…
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Attention to LLM Architectures: An Intelligent ML Engineering Guide
The article presents an informed view on different architectures used in modern language models (LLMs), including Encoder-Decoder, Encoder-Only, and Decoder-Only models. It defines their functions and specific uses. While the less complex Decoder-Only models like GPT have achieved excellent results, the article suggests that the choice of model should depend on the end application and…
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Not-So-Large Language Models: Good Data Overthrows the Goliath
In this article, we will see how Language Models (LM) can focus on better data and training strategies rather than just brute size to achieve LLM-like results (sometimes even better) and how people are already doing it successfully and democratically.
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Multithreading vs. Multiprocessing in Python (Part 2) – A Data Science Example
In the first part of this series we saw the differences between multiprocessing and multithreading in Python. Although we saw some examples there, I think it deserves some practical view on how it can be used on a real data science project.
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Multithreading vs. Multiprocessing in Python (Part 1)
Python is often regarded as an almost obscene programming language. A libertarian place where everything is permitted and our wildest dreams come true. Forget about labels and definitions, and, above all, brackets. It is a language that welcomes everyone, from the most experienced programmer (that weird guy that still uses Fortran for some reason you…
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