A narrative walkthrough of a single fine-tuning project on a real platform, covering the situation, the decision, the execution, the measured outcome, and the lessons.
Annotation tooling attracts more folklore than almost any part of the ML pipeline. Here are the durable misconceptions, why they spread, and what the evidence actually shows.
Walk through specific scenarios where teams fine-tuned models on real platforms, what made each project succeed or stall, and the patterns you can borrow.
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