Phase 8: Launch, Metrics & Scaling AI Products · 40 min · Python

Cross-Functional Leadership for AI Products

The PM who stops talking to ML engineers after launch is the PM whose feature quietly degrades for three months before anyone notices.

Hiring signal: Candidates who can describe running a model review meeting, an eval review meeting, and navigating a disagreement between a PM and an ML lead on a ship/hold decision demonstrate the cross-functional operational maturity that AI PM roles require — and that behavioral interview rounds specifically test for.

What you will learn

Partner with ML engineers and data scientists post-launch, not just during build
Run a model review meeting: what it covers, who owns what, and how often it should happen
Run an eval review meeting: turning eval deltas into ship/hold decisions
Navigate escalation and decision rights when a PM and an ML lead disagree on a launch call

The Problem

A team ships an AI-powered content recommendation feature. During the build phase, the PM and ML lead met daily — discussing model architecture, eval results, tradeoffs, and timelines. The feature launches successfully. The PM moves on to the next project. The ML lead goes back to model improvements. Three months later, the PM is surprised to learn in a quarterly review that the feature's click-through rate has dropped 22% because the model drifted (new content types emerged that the model wasn't trained on), and nobody escalated it because "the PM wasn't in the model review meetings anymore" and "we assumed someone else was watching the metrics." The feature that launched with celebration is now underperforming, and the PM — who should have been the first to know — was the last.

This is the post-launch partnership gap. During build, the PM and ML team are tightly coupled: daily standups, shared Slack channels, joint decision-making. After launch, the PM often disengages — "the ML team has it from here" — and the ML team often stops communicating upward — "the PM moved on, they don't need to know." Both assumptions are wrong. Post-launch is when the model is most vulnerable (it's encountering real-world data for the first time), and the PM is the bridge between model health and business impact. Disengaging means losing visibility into a critical aspect of product quality.

Post-Launch Partnership: What Changes

The partnership doesn't end after launch — it changes in character:

Dimension	Build Phase	Post-Launch Phase
Frequency	Daily	Weekly or biweekly (model review)
Focus	Building the model	Monitoring, maintaining, improving the model
PM's role	Define requirements, prioritize features	Translate model metrics to user impact, make ship/hold decisions
ML's role	Build, train, evaluate	Monitor, retrain, diagnose regressions
Key meetings	Design reviews, eval reviews	Model reviews, eval reviews, incident response
Decision type	"What should we build?"	"Should we ship this model update? Should we retrain?"

The PM's post-launch job is not to stop engaging but to engage differently: less "what should we build?" and more "is what we built still working, and what should we do about it?"

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Model Review Meeting, The Eval Review Meeting, Escalation and Decision Rights, Build It, What to Practice — plus a hands-on lab, quiz, and project artifact.

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Key terms in this lesson

Latency