ML - LearnXops (Page 2)

Monitoring AI in Production: Drift, Quality & Evals

LLM evaluation frameworks, output monitoring with Langfuse/Helicone, drift detection(Day 25)

Semantic caching, request batching, model cascading — techniques to slash AI infra bills (Day 24)

When to use managed vs self-hosted, a cloud-by-cloud breakdown for MLOps teams (Day 23)

Model versioning, prompt versioning, evaluation pipelines — the new ops discipline for AI (Day 21)

Auto-generating changelogs, test suggestions, deployment summaries, and anomaly detection (Day 20)

Real-world learning, production-grade projects, and AI insights for modern DevOps engineers