I'm Mayank, a solution architect who builds AI systems and cares about showing they work. My route here was sideways. Electronics, then software engineering, then systems architecture, picking up machine learning along the way. AI systems are systems first. The questions that decide whether one succeeds, how it fails, what it costs, who trusts it, are architecture questions in new clothes.
What I keep coming back to is the evidence layer. Building something that demos well is easy. The craft is in the measurement and calibration. Metrics whose assumptions match the problem. Splits that don't mislead. Saying precisely how well a system performs and where it stops. Most of what I write here is about that, and about bringing the same rigour to AI based systems.
Elsewhere in my head
Decision science, probability and connecting the dots. What I always find interesting is when something forbidding turns out to be simple from the right angle. Euler's formula looks like it's about imaginary numbers. It's really just saying a circle, viewed side-on, is a wave. The hard-looking version and the obvious version are the same statement. Most hard things have an angle like that. The work is finding it.
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