I gave the same 6,497 wines to two models and asked them different questions
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Most ML tutorials stop at a notebook with a green R² cell and a shrug. I wanted to go one step further: take two models I'd actually trained, and turn them into something you can poke at — a typed API and a little web app anyone can open. So I built sommelier-api : one dataset, two questions, two surfaces. Two lenses on the same wine The UCI Wine Quality dataset (Cortez et al., 2009) has 6,497 wines — 1,599 red, 4,898 white — each with 11 physicochemical measurements (acidity, residual sugar,…
1Key Takeaways
- Most ML tutorials stop at a notebook with a green R² cell and a shrug.
- I wanted to go one step further: take two models I'd actually trained, and turn them into something you can poke at — a typed API and a little web app anyone can open.
- So I built sommelier-api : one dataset, two questions, two surfaces.
- Two lenses on the same wine The UCI Wine Quality dataset (Cortez et al., 2009) has 6,497 wines — 1,599 red, 4,898 white — each with 11 physicochemical measurements (acidity, residual sugar,….
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that most ML tutorials stop at a notebook with a green R² cell and a shrug.
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