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2024

  • 4 min read

Benchmarking scikit-learn across python versions using uv

When python 3.11 came out 2 years ago (24 October 2022) it promised to be 10-60% faster than python 3.10, and 1.25x faster on the standard benchmark suite (see the what's new in 3.11). I've always wondered how that translates to training machine learning models in python, but I couldn't be bothered to write a benchmark. That is, until astral released uv 0.4.0 which introduces "a new, unified toolchain that takes the complexity out of Python development".

  • 4 min read

Thoughts on Constrained Intelligence

In my career I've focused mostly on applying what is now called 'traditional machine learning': regression, classification, time series, anomaly detection and clustering algorithms. You could frame machine learning as applying an algorithmic 'constrained intelligence' to a specific business problem. The challenge has always been to 'unconstrain the intelligence' (f.e. by tuning hyperparameters) and to further specify the business problem (proper target definition, clean data, proper cross validation schemes). The advent of large language models is starting to flip the equation; from 'unconstraining' intelligence to 'constraining' it instead.