Knowledge Graph — Coursera Notes › Academic disciplines › Information Technology / Computer Science › Artificial Intelligence › Deep Learning › Model Training
Model Training Monitoring
concept · part of Model Training
Monitoring and logging are essential for successful machine learning model training. They provide insights into how well a model is learning, whether any problems arise, and how training can be optimized. Without proper monitoring, training could proceed with unnoticed issues, leading to wasted resources and suboptimal models. Logging keeps a record of everything that happens during training—such as metrics, parameters, and system events—making it possible to diagnose issues and reproduce results.
Key metrics to track during model training include training loss (should generally decrease), validation loss (if it diverges from training loss, overfitting may occur), accuracy, precision, recall, F1-score (especially for imbalanced datasets), learning rate, and resource usage (CPU, GPU, memory).
Best practices include using visualization dashboards (e.g., Azure Machine Learning, TensorBoard) to view training metrics over time and quickly identify anomalies; setting automated alerts to notify if training loss does not decrease for a set number of epochs or if resource usage reaches critical levels; and granular logging of training parameters, dataset versions, model architecture, and system conditions to ensure reproducibility and facilitate comparison of experiments.
Inside Model Training Monitoring (6)
- Automated Alerts — Automated alerts can be configured to notify if training loss does not decrease for a set number of epochs or if resource usage reaches critical levels.
- Granular Logging — Detailed logging of all aspects of the training process, including parameters, configurations, and system conditions, helps in reproducing results and troubleshooting.
- Learning Rate — The learning rate is the step size used by the optimization algorithm to adjust model weights during training.
- Resource Usage Monitoring — Monitoring resource usage helps ensure efficiency and prevents bottlenecks.
- Training Loss — Training loss is a measure of how well the model is learning from the training dataset.
- Validation Loss — Validation loss is a measure of how well the model generalizes to unseen data.
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