What trade-offs decide managed ML platforms versus open-source Kubernetes?
WHAT IT TESTS: Ops overhead vs speed for ML infra. ANSWER OUTLINE: Weigh total cost plus hidden engineering headcount, lock-in vs flexibility, and audit feature gaps. RED FLAG: Recommending open-source purely to cut cost while ignoring the 2-4 person tax.
WHAT IT TESTS: Your ability to balance operational overhead, vendor lock-in, and time-to-market when selecting ML infrastructure for a mid-sized company. ANSWER OUTLINE: First, compare total cost of ownership beyond sticker price, including the hidden platform engineering headcount of 2-4 engineers for open-source stacks. Second, evaluate vendor lock-in against multi-cloud or data-sovereignty requirements. Third, assess managed feature gaps versus the flexibility of Kubernetes-native tooling like Kubeflow and KServe.
Read the original → mlai.qa
- #mlops
- #infrastructure
- #cloud
- #kubernetes
- #platform-engineering
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