Building an AI model is only half the job. Keeping it running reliably, affordably, and safely in production is where the real work begins. These guides cover the operational side of AI systems, from deploying models into production environments to monitoring their performance, managing costs, and responding to incidents when things go wrong. You will learn about MLOps practices that bring DevOps discipline to machine learning, model versioning and rollback strategies, observability tools that catch model drift before it affects users, and cost management techniques that prevent cloud bills from spiralling. The topic also covers scaling strategies for handling variable workloads, CI/CD pipelines for model updates, and runbook patterns for AI-specific incidents. Whether you are a platform engineer building AI infrastructure, an SRE responsible for AI system reliability, a DevOps practitioner adding ML workloads to your stack, or a team lead planning your AI operations strategy, these guides give you the practical knowledge to run AI systems that are dependable, cost-effective, and ready for the real world.
Learn to manage and optimize AI costs. From usage tracking to cost optimization strategies—practical guidance for keeping AI spending under control.
Learn the stages of deploying AI systems safely. From staging to production—practical guidance for each phase of the AI deployment lifecycle.
Learn to respond effectively when AI systems fail. From detection to resolution—practical procedures for managing AI incidents and minimizing harm.
Production AI requires continuous monitoring. Track performance, detect drift, alert on failures, and maintain quality over time.
Apply MLOps practices to LLMs: versioning, CI/CD, monitoring, incident response, and lifecycle management for production AI.