Core concept · Foundation

Data Collection, Logging & Data Quality

Trace a product event into a trustworthy training example.

Content reviewed September 2026Read to the end

What you will learn

Trace a product event into a trustworthy training example.

Decision trigger

Which missing event or quality observation would invalidate the training set?

Interview impact

Logging choices determine label validity, counterfactual gaps, debugging, and privacy scope.

Event to example

1Decision
2Eligibility
3Exposure
4Outcome
5Validated example

Mental model

A training row is only as trustworthy as the events and context that produced it.

A recommender records clicks but not which items were shown, so missing interactions cannot be interpreted as negatives.

How it works

Log decisions, eligible candidates, exposures, positions, context, outcomes, and event time with versioned contracts.
Validate schema, ranges, missingness, volume, freshness, duplicates, and late arrival before examples publish.

Product consequences

Missing exposure data makes ranking and recommendation feedback ambiguous.

Operational consequences

Schema owners, retention policy, replay, backfill, and quality alerts become production dependencies.

Alternatives

Sample events when full fidelity is too costly, but preserve known inclusion probabilities.
Aggregate at source when raw events add no justified value and increase privacy risk.

Failure modes

Client versions emit incompatible event meanings.
Late or duplicated events silently corrupt labels and aggregates.

Worked example

A recommendation impression logs request, user/session, eligible set, shown items, positions, policy version, and timestamp.
Clicks and later outcomes join only after validation and a defined maturation window.

Interviewer probes

How do you detect a broken mobile logger?
What data can you avoid retaining?

Expected depth by level

Entry: Name the core events and identifiers.
Mid-level: Add contracts, validation, lateness, and lineage.
Senior+: Define ownership, consent, repair, backfill, and policy evolution.
Role emphasis: SWE-ML emphasizes ingestion reliability; MLE connects it to examples and monitoring.

System Design prerequisites

Change data capture Event streaming

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.
TensorFlow Data ValidationTensorFlow · official documentation · reviewed 2026-09-04
Schema validation, training-serving skew, and drift detection.

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…

Previous: Product Problem Formulation & Baselines

Loading Engineering Foundry…

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.

TensorFlow Data ValidationTensorFlow · official documentation · reviewed 2026-09-04

Schema validation, training-serving skew, and drift detection.

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