Running AI and machine learning workloads on cloud accounts
The compute is the easy part. Data movement, checkpointing and idle time are what decide the bill.
Cloud Insights
AI, data, containers and the workloads teams actually run.
The compute is the easy part. Data movement, checkpointing and idle time are what decide the bill.
Build infrastructure has a different risk profile from production. Give it its own boundary.
Separate accounts per client are less about security theatre and more about clean endings.
Research workloads are bursty, deadline-driven and usually funded by a fixed number. That shapes the approach.
Latency and bandwidth dominate. Most other considerations are secondary.
A modest site does not need a complicated platform. It does need a handful of things done correctly.
Analytics workloads have sharp cost edges. Most of them are about how much data you scan.
A load test that takes out a shared dependency has told you something, but not what you wanted to know.
A pilot exists to find the unknowns cheaply, not to prove a decision already made.
An untested recovery plan is a document. A drill turns it into a capability.