ml-solution-design

Turn customer requirements into an ML solution architecture. Covers requirements intake (business goals, data reality, SLOs, budget, team skills, compliance), decision frameworks (batch vs real-time vs streaming inference, managed SageMaker/Bedrock vs self-hosted K8s/vLLM, build vs buy, classical ML vs LLM vs hybrid), reference architectures, cost estimation, and engagement deliverables. Use when starting a customer engagement, gathering ML requirements, choosing an architecture, writing a solution proposal, deciding batch vs real-time, SageMaker vs self-hosted, or scoping an MLOps/LLMOps delivery.

Version

1.0

License

Apache-2.0