Model execution logs

Overview

The Execution Logs dialog (open via ⚙️ > Logs) records every model run across your workspace. It captures metadata such as start/end time, model type, hyper-parameters, and test-set metrics—providing a single place to verify, audit, and debug model training at scale.


Key Features

Column
What it tells you

Start time / Finished time

UTC timestamps marking the beginning and end of training.

Duration

How long the run took (e.g., 2m37s).

Status

done-ok, error, or running; useful for spotting failures quickly.

Model name & Model Type

Friendly name plus classification / regression / time-series, etc.

Model Code

Unique 12-character hash—required for API calls (/prediction, /fetch-result, etc.).

Tenant code

Internal workspace identifier (visible for multi-tenant admins).

Actionable Insights goal

“Show Value…” link with the text prompt you supplied when enabling AI insights.

Model advanced run parameters

All non-default parameters—outlier threshold, collinearity cutoff, imbalance handling, etc.

Metrics for test dataset

F1, Accuracy, AUC for classification; R², MAE, RMSE for regression/time-series.

Trained model hyper-parameters

Captures grid-search results or any user-defined hyper-settings.

Dataset shape

Rows and columns fed into the trainer after preprocessing.


  • Use the 🔍 field beneath each header to search by model name, code, or date.

  • Click the funnel icon to show only errors, a specific model type, or a date range.


Typical Workflows

  • Confirm completion —Refresh logs to ensure today’s run shows done-ok.

  • Grab model code for API endpoints without opening the model UI.

  • Compare durations to detect unusually long or short runs, hinting at data issues.

  • Audit hyper-parameters before sharing results with stakeholders.

  • Investigate failures (error status) and cross-reference with advanced parameters.


Best Practices

  • Refresh first – Click Refresh Logs after a run to pull the latest status.

  • Export for audit – Copy rows or take a screenshot before purging old models.

  • Track trends – Rising durations or frequent errors can indicate growing data size or schema drift.

  • Secure access – Only Admins can view logs; restrict role permissions if needed.

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