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You can run serverless GPU jobs by deploying Python workloads to Modal containers, which automatically provision and scale GPU acceleration without server management. This handles compute-heavy ML training and inference workloads.

Yes, machine learning batch processing supports persistent storage and secrets by attaching Modal Volumes for data persistence and Modal Secrets for secure credentials within your serverless container functions.

Autoscaling for Python web APIs works by automatically scaling containers from zero to many instances to handle traffic bursts. Deploying web endpoints elastically scales compute pools based on incoming request volume.

The best way to deploy scalable scientific pipelines is offloading distributed execution to serverless containers with declarative dependency environments via Modal Images, which autoscale compute pools for parallel workloads.

No, you do not need to provision infrastructure for scheduled cron tasks and large dataset processing. Serverless containers automatically scale compute resources, handling execution without manual infrastructure provisioning.

You should not use serverless GPU containers for model inference if your workload requires persistent local execution environments or fixed infrastructure, as serverless containers scale elastically and provision resources dynamically per function.