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Module
Module 1 of 5Lesson 1 of 1~3 min

Course introduction

This course is for PMs who need to launch an AI feature and then stay in control once it is in users' hands. Over four modules, you build a dashboard covering quality, cost and latency, along with a procedure for switching models without unpleasant surprises in production.

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Lesson objective

By the end of this introduction, you will know what you are going to produce (a quality, cost, latency dashboard and a model change procedure) and in what order the four modules get you there.

Topics covered

  • AI monitoring
  • LLMOps
  • shipping AI to production
  • LLM observability
  • AI feature

Where it fits

Overview

What will I set up in this course, and in what order?

Lessons in this module

  1. Course introduction (this lesson)

What you will learn in the course

This lesson is part of the course Ship and monitor an AI feature in production

  • Design the staged launch of an AI feature (feature flag, shadow, canary, percentages) with quantified promotion criteria and a tested kill switch.
  • Define what you log and trace for each model call, what you mask or exclude (personal data), how long you keep it, and with which tool.
  • Set up quality signals in production (user feedback, sampled human review, online evaluations, drift) and connect them to the evaluation set.
  • Manage cost per request and per useful outcome, and perceived latency (budgets, alerts, caching, streaming, response length, model choice).
  • Diagnose AI-specific incidents (drift, provider outage, deprecation, call loop, cost spike) and plan the right fallbacks.
  • Run a model or prompt change with regression evaluations, shadow comparison, staged rollout and a rollback plan.
  • Build the quality, cost and latency dashboard of an AI feature and report to stakeholders with decisions to make.

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