Our predictive analytics models in Mode Analytics are scheduled to run every 6 hours to generate demand forecasts. After deploying to cloud infrastructure, the scheduler is missing triggers randomly - sometimes it runs, sometimes it doesn’t. When it does run, there’s no error, but we’re seeing gaps in our forecast data. Cloud scheduler logs show:
Scheduled: 2025-06-18 06:00:00 UTC
Executed: Not triggered
Next run: 2025-06-18 12:00:00 UTC
Resource allocation seems adequate (8 CPU, 32GB RAM). No alerting for missed triggers exists, so we only discover gaps when business users complain. This is causing forecast delays that impact inventory planning. Has anyone experienced reliability issues with Mode’s predictive analytics scheduler in cloud environments?
Cloud schedulers often have different reliability characteristics than on-prem cron jobs. Check if your cloud provider’s scheduler service has any maintenance windows or known issues during the times you’re seeing missed triggers. Also verify that Mode Analytics has persistent storage for the scheduler state - if the scheduler state is in-memory and the container restarts, it might lose track of pending jobs.
You mentioned no alerting exists for missed triggers. That’s a critical gap. Implement dead man’s switch monitoring - set up an external monitor that expects a heartbeat from your predictive analytics job every 6 hours. If no heartbeat is received within 6.5 hours, trigger an alert. Tools like Cronitor or Healthchecks.io are built for this. This way you discover missed runs immediately, not when users complain about stale forecasts.