An ML engineer's guide to AI infrastructure
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A PoC that runs clean jobs on a small cluster tells you very little about whether that infrastructure will hold once your workload is in production.
This field guide, from Crusoe's Solutions Engineering team, walks through what a PoC should actually test at every stage: defining success criteria before you start, running the right baseline tests, reading results honestly, and what needs to be locked down before you cut over.
It's not a vendor comparison. It's a way to make sure your PoC surfaces the real problems now instead of after you've committed.
Inside, you'll find:
This field guide, from Crusoe's Solutions Engineering team, walks through what a PoC should actually test at every stage: defining success criteria before you start, running the right baseline tests, reading results honestly, and what needs to be locked down before you cut over.
It's not a vendor comparison. It's a way to make sure your PoC surfaces the real problems now instead of after you've committed.
Inside, you'll find:
- The success-criteria mistakes that make PoC results tough to trust
- Which baseline tests to run before your first real workload
- How to tell a fixable configuration issue from a structural one
- The production cutover checklist most PoCs skip