Camilo Andrés Cáceres Flórez

Camilo Andrés Cáceres Flórez

Staff ML Engineer (Applied AI), Data Science Expert, and AI Architect leading GenAI, data science, and MLOps at scale. Ph.D. in Mechanical Engineering (Mechatronics) from UNICAMP and EMBA in Strategic Leadership, with experience driving end-to-end ML platforms, computer vision, and data science initiatives.

Production ML systems at scale: control planes, contracts, and safety nets

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Published:

Production ML systems behave like distributed services, not offline experiments. A control-plane mindset keeps models shippable: contracts define what is safe to launch, rollbacks are rehearsed, and telemetry is the default—not an afterthought.

What the control plane owns

  • Contracts: schemas, validation steps, and acceptance checks before a model ever touches live traffic.
  • Rollout states: shadow, canary, and full production with clear promotion/demotion criteria.
  • Signals: golden datasets, automated backtests, and live health indicators wired to alerts.

Delivery path that survives change

  1. Shadow and compare. Route a small slice of production requests through the new model in shadow; record deltas against golden signals.
  2. Guarded canary. Promote only if statistical deltas stay within allowed bounds; attach rollback playbooks to the same decision.
  3. Runtime quality. Keep monitors at the feature, model, and business layers to spot drift or cascading failures.

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Ads ML as a subtopic of production ML systems

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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.