What Happens When Your Managed ML Platform Is Not Available in a Regulated Region
A practical guide to managing ML workloads when cloud ML platforms are unavailable in regulated regions due to compliance and data residency rules.
Managed ML platforms become unavailable in regulated regions, causing teams to face several challenges. These include a drop in experimentation velocity, difficulty accessing GPUs, drift in runtime environments, increased complexity in access control, potential violation of data residency rules, reduced reproducibility, weakened cost control, and ambiguous operational ownership.
The issue lies in the fact that the team's ML workflow is dependent on a region-bound abstraction, which cannot be easily reproduced. When this abstraction fails, the entire ML execution layer suffers, leading to a fragile system when the company expands across jurisdictions. To mitigate these risks, the company should focus on owning a critical layer that allows ML work to run safely in the required region.
This includes ensuring region-local execution, where experiments and GPU workloads can run where the data is located, and maintaining reproducibility and compliance even in the face of provider service unavailability.
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