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.
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.
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.