experiment-tracking

To track ML experiment metrics in Snowflake, initialize an ExperimentTracking session to organize runs and metadata. This automates metric logging across frameworks like XGBoost, Keras, and LightGBM for reproducibility and comparison.

Yes, you can auto-log metrics for XGBoost, Keras, and LightGBM training runs. The skill captures initialization and automatic logging to enable reproducibility and comparison across these common machine learning frameworks.

Before logging ML experiments, you need Python, an active Snowflake connection, and the ExperimentTracking API. These prerequisites allow you to initialize runs, log parameters and metrics, and manage the experiment lifecycle.

To manually log parameters and manage the ML experiment lifecycle, use the ExperimentTracking API to log specific metrics, end active runs, and delete experiments. This provides full control over your training metadata.

Yes, this approach supports comparing model performance across different frameworks. By organizing runs and auto-logging metrics from XGBoost, Keras, and LightGBM into Snowflake, it enables direct reproducibility and performance comparison.