Core concept · Foundation

Product Problem Formulation & Baselines

Decide whether ML is justified before selecting a model family.

Content reviewed September 2026Read to the end

What you will learn

Decide whether ML is justified before selecting a model family.

Decision trigger

What measured baseline gap, workload, or harm pattern would justify introducing ML?

Interview impact

A precise decision unit and baseline keep the rest of the design measurable and prevent architecture-by-buzzword.

Decision before model

1Product need
2Decision and action
3Rules or simple baseline
4Measured gap
5ML candidate

Mental model

Define the product action first; the model is only one possible way to support it.

A fraud team asks for AI to prioritize a review queue, but simple rules may already meet the first release's capacity and harm constraints.

How it works

Name the user, stakeholder, decision, prediction unit, action, and reversible fallback.
Compare rules, a domain algorithm, and a simple model against latency, freshness, scale, and harm constraints.

Product consequences

A baseline defines what useful improvement means and exposes when automation is unnecessary.

Operational consequences

Every added learning component creates data, deployment, monitoring, and rollback ownership.

Alternatives

Use deterministic policy when the decision is fully specified and stable.
Use human triage when examples are scarce and review capacity is the dominant constraint.

Failure modes

The prediction target is easy to label but unrelated to the desired action.
The model adds latency while producing no decision improvement over rules.

Worked example

Start with rules that route high-value, high-velocity transactions to review.
Add a scorer only after measuring unresolved queue ordering and label maturity.

Interviewer probes

What happens without ML?
Which requirement would change your baseline choice?

Expected depth by level

Entry: Define one decision and a defensible baseline.
Mid-level: Connect the baseline to data availability, serving, and fallback.
Senior+: Challenge the ML premise and define organizational ownership for evolution.
Role emphasis: SWE-ML emphasizes integration; MLE balances the lifecycle; Applied Scientist deepens objective validity.

System Design prerequisites

Requirements and constraints

Sources and review notes

These sources support the technical claims on this page. Product-specific examples remain Engineering Foundry teaching scenarios.

Rules of Machine LearningGoogle for Developers · first-party engineering/science · reviewed 2026-09-04
Baselines, instrumentation, launch sequencing, and production iteration guidance.

Reach this point to mark the lesson as read.

Finish this lesson

Record completion when you have worked through the material. Completion is learning progress, not evidence of mastery.

Loading completion…

Loading Engineering Foundry…

Expected depth by level

Entry

Define one decision and a defensible baseline.

Mid-level

Connect the baseline to data availability, serving, and fallback.

Senior+

Challenge the ML premise and define organizational ownership for evolution.

Role emphasis

SWE-ML emphasizes integration; MLE balances the lifecycle; Applied Scientist deepens objective validity.

Sources and review notes

These sources support the technical claims on this page. Product-specific examples remain Engineering Foundry teaching scenarios.

Rules of Machine LearningGoogle for Developers · first-party engineering/science · reviewed 2026-09-04

Baselines, instrumentation, launch sequencing, and production iteration guidance.

Decision trigger

Interview impact

Mental model

How it works

Product consequences

Operational consequences

Alternatives

Failure modes

Worked example

Interviewer probes

Expected depth by level

System Design prerequisites

Practice this concept

Sources and review notes

Finish this lesson

Decision trigger

Interview impact

Mental model

How it works

Product consequences

Operational consequences

Alternatives

Failure modes

Worked example

Interviewer probes

Expected depth by level

System Design prerequisites

Practice this concept

Sources and review notes

Finish this lesson