Azure Data Scientists Associate Practice Exam

Using `mlflow.log_metric()` is the preferred method for storing metrics during model training because it is specifically designed to seamlessly integrate with MLflow, a popular open-source platform for managing the machine learning lifecycle. This function allows data scientists to log various metrics associated with their models directly to MLflow Tracking.

Logging metrics with MLflow provides several advantages:

1. **Centralization**: Metrics are stored in a centralized tracking server, which allows for easy access and comparison. This is important when iterating on model training, as it helps in identifying which hyperparameters or configurations yield the best performance.

2. **Versioning**: MLflow automatically version controls the metrics, so you can track how your model's performance evolves over time or across different experiments. This feature is crucial when tuning models or conducting evaluations over numerous iterations.

3. **Visualization and Monitoring**: MLflow offers built-in capabilities for visualizing metrics, enabling data scientists to generate graphs and insights more efficiently. This allows for better understanding and communication of model performance to stakeholders.

4. **Integration**: Since MLflow supports multiple programming languages and frameworks, using `mlflow.log_metric()` fits well into a wide range of machine learning workflows and tools.

In contrast, using a text file