1.Catch incoming prediction batch
Integrate Webhooks by Zapier, payload mapping tools, and metadata handling to receive the prediction batch and map batch details.
Log model evaluation metrics to central results sheet
Automatically catch incoming prediction batch hooks across Webhooks by Zapier, Google Drive, Code by Zapier, Filter by Zapier, and Google Sheets. Create and update when evaluations compute RMSE metrics and pass filtersβso you can log results and review failures and share workshop insights without manual logging.
How this automation logs model evaluation metrics
When incoming prediction batch hooks arrive, delays can block workshop review and obscure model regressions. This automation receives batches, finds the targets file, runs Python evaluation, filters failures, and appends metrics to Google Sheetsβso your team can track RMSE without chasing files.
Webhooks by Zapieror swap with your favorite app
2.Find configured targets file
Integrate Google Drive, file search, and Drive access to locate the configured targets file identifier for evaluation.
Google Driveor swap with your favorite app
3.Run Python to compute metrics
Integrate Code by Zapier, Python execution, and JSON parsing to fetch targets, slice the window, and compute RMSE.
Code by Zapieror swap with your favorite app
4.Filter for successful evaluations
Integrate Filter by Zapier, validation checks, and sentinel handling to continue only when metrics are valid for recording.
Filter by Zapieror swap with your favorite app
5.Create row with evaluation outputs
Integrate Google Sheets, spreadsheet append, and column mapping to create a new row with RMSE metrics and metadata.
Google Sheetsor swap with your favorite app
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