Continuous Fine-Tuning — Regular Model Updates From New Data

I will build a continuous fine-tuning pipeline — automatically collecting new training examples from production feedback, running quality filtering, triggering fine-tuning jobs when sufficient new data accumulates, evaluating the new model against the current production model, and deploying the improved model when it passes evaluation gates. Continuous fine-tuning converts your production AI system into a self-improving asset — every interaction that generates a human-reviewed correction becomes training signal for the next model version, systematically improving quality over time rather than relying on a static model that degrades relative to evolving user expectations.

The pipeline covers production feedback collection, quality filtering for new training examples, automated training job triggers, evaluation gate configuration, automated deployment on passing evaluation, and a fine-tuning cycle dashboard tracking model improvement over time.