Practical patterns and playbooks for deploying, observing, versioning, retraining, and controlling costs for models in production.
Practical runbook for detecting, triaging, containing, and resolving production model incidents — with severity guidance, immediate actions, rollback and safe-mode procedures, retraining triggers, telemetry to collect, stakeholder communication templates, and a postmortem template.
A practical, operational runbook with deployment patterns, observability and alerting guidance, drift-detection recipes, retraining triggers and safe retraining procedures, versioning and rollback practices, cost-control ideas, incident response templates, and SRE handoff guidance to keep production models reliable and maintainable.
Interactive, actionable checklist for moving ML models from prototype to production. Each checklist item includes a readiness yes/no, owner, and notes/acceptance criteria so teams can capture decisions, assign responsibility, and save the result to organizational memory.
A practical, ready-to-adapt template for continuous validation: test catalog, staged canary rollout steps, essential metrics and thresholds, automated sampling rules, drift detection settings, retraining patterns, and explicit rollback and approval criteria to keep production models safe and effective.
A practical, operational playbook describing pipeline architecture, sampling and signal strategies, canary and A/B evaluation patterns, automated retrain triggers, rollback rules, and human escalation paths for keeping models safe and useful in production.
Practical, outcome-focused guidance to decide whether a feature store is right for your team, design simple feature pipelines, keep training and serving parity, and operationalize feature versioning, testing, and monitoring.
Interactive checklist to evaluate feature engineering, discoverability, versioning, online/offline parity, freshness, testing, monitoring, governance, and operational runbooks for production-ready features.
A practical playbook to measure, attribute, budget, and reduce cloud and inference costs while preserving SLAs and user experience. Includes metrics, cost-attribution patterns, inference batching and caching guidance, autoscaling best practices, spot-instance strategies, budgets and alert templates, a rapid 30-day action plan, and governance controls.
Practical reference architectures, tradeoffs, and an operational checklist for batch, low-latency (online), streaming, and hybrid model serving. Includes patterns for request flows, caching, autoscaling, warmstart, versioning, SLO & security mapping, monitoring signals, and a short decision checklist to choose the right serving mode.