You will apply batch and mini-batch training procedures to optimize model convergence.

You will analyze training logs and loss curves to diagnose common model training issues.

You will evaluate the impact of class-imbalance techniques on model performance.

You will apply appropriate performance metrics to evaluate machine learning models.

You will analyze confusion matrices and residual plots to identify systematic model prediction errors.

You will evaluate the statistical significance of differences in metrics.

You will apply validation techniques to assess model performance on unseen data.

You will analyze A/B test or shadow deployment results to compare new model performance against a baseline.

You will evaluate model-drift indicators to trigger retraining workflows.

In this project, you will design and implement a machine learning model evaluation and monitoring framework for a production system. A technology company has deployed a recommendation model that predicts user engagement with content, but its performance has become inconsistent due to potential data drift and evolving user behavior. Your task is to build an evaluation pipeline that compares model versions, analyzes prediction errors, and monitors performance stability over time. You will train baseline and improved models, analyze training logs and loss curves to verify convergence, evaluate class-imbalance handling techniques to ensure fair evaluation across classes, evaluate them using appropriate metrics, analyze errors with confusion matrices and residual plots, perform statistical comparisons, simulate monitoring scenarios such as A/B testing or shadow deployments, calculate drift indicators like Population Stability Index (PSI), and define conditions for model retraining. The final deliverable is a modular Python evaluation framework along with a written engineering explanation demonstrating how evaluation insights support reliable model deployment decisions.