production

A model that has stopped working returns answers with the same confidence as one that still does. This lesson covers the difference between data drift and concept drift, what to monitor when labels arrive months late, the triggers that should start a retraining run, and the cases where retraining is the wrong response.

The most common way a good model fails in production is that the features it is served differ from the ones it was trained on. This lesson covers where that divergence comes from, why point-in-time correctness is harder than it looks, what a feature store actually solves, and when you do not need one.

A trained model is a small box inside a large system, and the system is what decays. This lesson covers the failure modes that are specific to machine learning: why changing one feature moves every weight, why hidden consumers break silently, and why data dependencies cost more than code dependencies.

Turning a voice AI prototype into a real product means facing hard choices. Learn the two competing architectures (the debuggable cascaded pipeline versus end-to-end speech-to-speech models), how function calling lets an agent actually do things, why cost and observability drive real decisions, the failure modes that break voice agents in production, and how to evaluate whether one actually works.

Move beyond toy ReAct loops. Learn how to build a production-grade agent harness with a robust control loop, tool registry, schema validation, retry logic, token budgets, abort signals, and a persistent journal that survives crashes.

What it takes to ship a generated mesh into a real game, AR app, or animation: topology cleanup, retopology, LODs, auto-rigging, and the 2026 platform landscape across Meshy, Tripo, Rodin, CSM and others.