chore: delete items

Author: DiogoRibeiro7

_posts/-_ideas/2030-01-01-data_model_drift.md

@@ -14,11 +14,6 @@ tags: []
 ## Article Ideas on Data Drift and Model Drift
 
 
-
-### 4. **Techniques for Monitoring and Managing Model Drift in Production**
-   - **Overview**: Discuss best practices for monitoring model performance over time to detect and mitigate model drift.
-   - **Focus**: Real-time model monitoring, automated alerts, and retraining strategies to keep models performant. Introduce tools like **MLflow**, **Seldon**, and **TensorFlow Extended (TFX)**.
-
 ### 5. **Model Retraining Strategies to Handle Data Drift**
    - **Overview**: Provide strategies for handling data drift through **incremental learning**, **active learning**, or **periodic retraining**.
    - **Focus**: Pros and cons of different retraining approaches, and how to avoid overfitting or underfitting when adapting models to new data distributions.