Your question is Design ML Architecture for Legacy Data. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are helping modernize a client platform that relies on legacy systems, fragmented data sources, and manual business workflows. The client wants a scalable ML data architecture that can support production use cases without forcing an immediate full replacement of existing systems.
How do you approach designing a scalable data architecture for a client with legacy constraints?