We are setting up a pipeline that spans on-prem servers and AWS. How does handle artifact storage in such a fragmented environment? Are there specific configurations to ensure data consistency for global AI applications?
3 answers
For a hybrid setup, the key is using a centralized artifact store like S3 or Azure Blob that all workers can reach. is quite flexible here; you just need to set your connection IDs correctly in the UI. In our global setup, we used a centralized tracking server but kept local Celery workers near the data to reduce latency. The most important thing is ensuring your IAM roles or access keys are correctly mapped across your on-prem and cloud environments to avoid "access denied" errors during large-scale data transfers.
That makes sense for storage, but what about task latency? If my scheduler is in the US and I'm training in Asia, will slow down my training loops due to network round-trips?
The setup is very robust for multi-region work. Just make sure your database backend for is high-availability, otherwise a single outage can stop all your logging.
Great point, Joseph. We used a managed SQL instance for our Airflow backend and it has been rock solid for over a year now. The reliability is definitely there for enterprise use.
Michael, the task heartbeat is very lightweight, so it shouldn't impact your training time much. However, if you are logging massive artifacts like 10GB model files, that’s where you’ll feel the bottleneck. A good strategy is to log metadata to the central Airflow DB but save large files to a regional bucket first, then sync them later. This keeps your global data pipelines moving fast without sacrificing visibility.