MLOps & AI Deployment

Most AI projects quietly die on the way to production. MLOps is what stops that. The discipline connects model development with production reliability and includes building CI/CD pipelines for models, A/B testing and canary deployments for AI features, monitoring for model drift and performance degradation, versioning and rollback for when things go sideways, and inference cost optimization that gets harder as traffic grows. In 2026 the field stretched to cover LLMOps too. That means prompt versioning, token cost tracking, and latency optimization for real-time applications where a slow response is the same as no response.