The Work Between an AI Demo and a Dependable Product
When a demo wows the room, we tell a story about possibility. Then the real work starts. I’m Maya Chen, and I’m supposed to turn a flashy demo into something people can rely on every day. Th
# AI Developers and Production
The human work of moving AI from a demo into a reliable product.
8 MLOps Practices That Make AI Changes Reproducible
The problem is the moment after the demo ends. Pressure from deadlines, the memory of brittle edge cases, and tired frontline staff all press in at once. Hope sits in a quiet belief: we can
Latency, Throughput, or Reliability: Which AI Benchmark Matters in Production?
I’m not here to pretend one number will save the day. I’m here to tell you what really moves the dial when real users show up with real needs and real patience. I’m Maya Chen, a software eng
A Threat Model for AI Systems That Are Already Live
I’ve learned that after the demo ends, the real pressure starts. The clock keeps ticking, and what you ship isn’t just code. It’s people’s trust, a thin line between usefulness and harm, and
Model Drift Is Not One Problem
I am Maya Chen. I am thirty-nine and I manage software engineers who turn a flashy demo into a dependable system. Today, we’re talking about drift, the slippery thing that wears away a model
Feature Stores, Data Quality, and Streams: What Production AI Actually Depends On
The demo wrapped yesterday. Today I’m sorting through the debris, and the path from sparkles to production looks less glamorous and more like plumbing. If you want a model that actually beha
Can Another Team Reproduce Your AI Release?
Can another team rebuild the data, environment, model, and evaluation from recorded evidence? That question sits at the center of every public demo we ship. The human reason it matters is si
The Hidden Cost of Fast AI Inference
The promise of faster AI inference is simple: get answers quicker, spend less per call, and scale without breaking budgets. The human reason it matters is plain. In a world where decisions h
9 Governance Decisions to Make Before an AI System Goes Live
The pressure is real. The demo looked glorious, but the user will press go the moment the clock ticks. The staff who keep the lights on will lean on this system, not the slide deck. I want a
Designing AI for the Person Who Must Correct It
I start with the need I meet every morning: a demo that promised polish but arrived wearing a rough edge. The project is billed as a gleaming AI assistant, yet I live with the person who mus