Ads ML as a subtopic of production ML systems

Ads ML shares the same control-plane skeleton as any production ML system; the difference is in the constraints. Bidding and pacing layers add strict latency and budget limits, but they still benefit from the same contracts, rollouts, and observability defaults.

Ads-specific considerations

• Marketplace health: enforce budget, pacing, and fairness rules at the control plane—not inside model code.
• Auction alignment: simulate auction outcomes before rollout; couple canaries with spend and win-rate guardrails.
• Creative experimentation: treat creative scoring and selection as pluggable policies behind the same interfaces.

Operational patterns that carry over

• Golden datasets for auctions and pacing events.
• Shadowing and replay for bidder changes.
• Paired rollbacks for model, creative policy, and pacing parameters.

Related reading

• Anchor post: Production ML systems at scale: control planes, contracts, and safety nets.
• Measurement side: Geo experiments for ads lift to validate business impact.
• Pillar hub: Production ML systems at scale.

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