Boosting MLOps: Mastering Model Retraining for Peak Performance

This article from Qiita AI dives into the crucial aspects of model retraining within MLOps, showcasing how to maintain and enhance prediction accuracy. It highlights proactive strategies for addressing data and concept drift, ensuring models remain robust and deliver maximum business value. The article provides a detailed guide on when and how to retrain models for optimal performance.

• •Model retraining is key for maintaining prediction accuracy in MLOps.
• •The article details different triggers for retraining, including data drift, concept drift, and business events.
• •Various data strategies like full retraining, incremental learning, and windowing are discussed for model retraining.