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Dive into the four pillars that organize the blog. Each link jumps to a curated hub with recommended posts.
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Operating GenAI safety and policy reviews
Lightweight processes to keep prompts, tools, and policies aligned as GenAI products evolve.
Evaluation blueprints for GenAI systems
Paired offline and online evaluations with safety, quality, and cost signals wired into delivery.
Backtesting ML pipelines before rollout
Golden runs, replay tests, and failure injection that catch regressions before canary.
Platform guardrails that keep ML services shippable
Contracts, validation gates, and rollback drills that make model delivery predictable across teams.
Auction and pacing simulations for ads lift
Counterfactual simulations that stress pacing, budgets, and auction dynamics before running expensive experiments.
Geo experiments for ads lift without slowing delivery
Designing geo experiments with CUPED adjustments, overlap checks, and playbooks teams can actually run.
Ads ML as a subtopic of production ML systems
How bidding, pacing, and marketplace constraints fit the same production ML architecture without a separate pillar.
Production ML systems at scale: control planes, contracts, and safety nets
A control-plane view for shipping ranking and ads models safely with contracts, staged rollouts, and observability.
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