One of the quiet themes running through this series is that many models don’t disappear because they are flawed, they disappear because our habits do not change and we refuse to learn outside of the ‘cannon’. We standardize workflows. We optimize for familiarity. And eventually, certain modelling assumptions stop being questioned. Random Forest is perhaps the clearest example of this phenomenon. It is the model we reach when we want something dependable. The model we deploy w

Or: What I learned about model compression by comparing myself to Michael Jordan The Problem Everyone Runs Into Last week, someone in my LinkedIn comments mentioned they were struggling to fine-tune a distilled OCR model. The model was excellent at table extraction with genuinely impressive performance. But when they tried to adapt it for their specific use case, it immediately overfitted and performed worse than baseline. This isn't unusual. I've seen this pattern repeatedly

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Worry not, I am obviously not talking about ghosting, the act of “abruptly ending communication with someone without explanation”. If...

Run, the Robots are coming! But hey, maybe they will LOVE you! It is quite fascinating to see some of the recent headlines since the...