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Gradual Rollout Strategies for AI Features
Deploying AI features to production requires careful risk management. Gradual rollouts help identify issues early while minimizing blast radius.
Risk Management
7 articles
1 min read
Operational Risk in AI Systems: Keeping the Lights On
Operational resilience is the unsung prerequisite for AI adoption. Practical SRE for AI means instrumenting model performance, bounding cost exposure, and…
1 min read
Model Risk Management for Large Language Models
LLMs are different beasts: unpredictable, context‑sensitive, and often opaque. Model risk management for LLMs needs to emphasise provenance, prompt…
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AI Risk Management: Identifying and Mitigating AI Risks
AI introduces risks that cut across data, models, operations and people. Over the past year I've helped teams map those risks to concrete controls — from…
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AI Governance Frameworks: Building Trust at Scale
Governance isn't a checkbox — it's what lets organisations scale AI safely. The frameworks I use combine risk tiers, model lifecycle controls, and pragmatic…
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AI Governance for Enterprise: Policies and Practices
AI governance must balance: Innovation : Enabling teams to use AI effectively Risk : Managing security, privacy, and compliance risks Consistency : Ensuring…
1 min read
Canary Deployments for ML Models
Canary deployment provides a controlled, observable approach to rolling out new model versions with minimal risk.