Why is waitForModel Not Detecting BigQuery ML Model Readiness in My Apps Script Pipeline?

Below is an explanation of the issue and some potential solutions.

---

Why the Wait Function Isn’t Enough

Your wait function checks for the model object (by its existence or by a non-null creationTime) and even sees a state of "ACTIVE." However, with BigQuery ML models the API’s notion of "active" may indicate that the model is registered in the system but not yet fully trained or fully available for all operations (like table-valued functions such as ML.DETECT_ANOMALIES). In other words, even though your wait function sees that the model exists, additional internal processing may still be occurring.

---

How to Ensure the Model Is Truly Ready

1. Poll for a More Definitive Status:
   Instead of only checking for creationTime or an "ACTIVE" flag, try using the BigQuery jobs API to poll for the completion status of the model creation/training job. The job metadata may provide a better indication that the model training has fully completed.

2. Delay Before Running Anomaly Detection:
   If a direct status check isn’t available, consider adding an extra delay (or a few additional polling cycles) after the initial check passes. Although not as clean, this extra waiting period can help ensure that any background finalization tasks are completed.

3. Verify Model Metadata:
   Where possible, inspect additional metadata from the model (if available) that might indicate readiness for prediction. Some ML models include fields such as a "trainingRun" or "lastModifiedTime" that change when the model has truly finished training.

4. Check with a Test Query:
   An alternative approach is to run a harmless query against the model (or a simple prediction) and only proceed if that query succeeds. This ensures that the model is fully available for queries like ML.DETECT_ANOMALIES.

---

Example Code Adjustment

Below is a pseudocode adjustment that adds an extra delay and optionally uses a test query:

JAVASCRIPTCopied
function waitForModelReady(projectId, datasetId, modelId, accessToken, timeout, interval) {
  timeout = timeout || 480000; // 8 minutes
  interval = interval || 5000; // 5 seconds

  var startTime = Date.now();
  
  // Poll until the basic check passes
  while (Date.now() - startTime < timeout) {
    if (modelExists(projectId, datasetId, modelId, accessToken)) {
      Logger.log('Model ' + modelId + ' found. Verifying readiness...');
      
      // Optionally run a simple test query to confirm readiness
      try {
        // Use a harmless query that will return a small result
        const testQuery = `SELECT 1 FROM ML.PREDICT(MODEL \`${projectId}.${datasetId}.${modelId}\`, (SELECT * FROM your_test_data LIMIT 1))`;
        fetchBigQuery(testQuery);
        Logger.log('Model ' + modelId + ' is fully ready');
        return true;
      } catch (e) {
        // Query failed; model might not be fully ready, so keep waiting
        Logger.log('Test query failed: model not fully ready. Retrying...');
      }
    }
    Logger.log('Waiting for model ' + modelId + '... Current state: ' + getModelState(projectId, datasetId, modelId, accessToken));
    Utilities.sleep(interval);
  }
  throw new Error('Timeout waiting for model ' + modelId);
}

In this version, once the initial check confirms the model exists, a test prediction query ensures the model is truly ready for operations like anomaly detection. Replace the test query with something suitable for your model (it should be lightweight and not affect production data).

---

Summary

• Reason: Your wait function checks for model existence, but BigQuery may mark the model as "ACTIVE" before it’s fully prepared for subsequent queries.
• Solution: Instead of checking only for existence and a basic state, either use the jobs API for detailed training status or perform an actual test query against the model. You may also insert an extra delay as a temporary workaround.

By adjusting your wait mechanism as above, you should be able to ensure your pipeline only attempts anomaly detection once the model is fully prepared.