Jan 16
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Your ML model can fail silently… and no dashboard will warn you. In MLOps Episode 6, we build REAL production-grade monitoring with FastAPI, Prometheus & Grafana — including drift detection, OOD alerts, model-version tracking, prediction distribution shifts, and full observability. What you’ll learn in this episode: ✔ Why model drift makes even a “perfect” model fail ✔ How real companies monitor AI in production ✔ How to track predictions, requests & text patterns ✔ Building a fully monitored ML microservice ✔ Setting up Prometheus metrics for ML ✔ Building Grafana dashboards for drift & behavior ✔ Deploying a 3-service stack with Docker Compose Tools we use: FastAPI Prometheus Grafana Docker & Docker Compose Python scikit-learn 👇 Comment below: Drift Happens if you’re learning MLOps with me 👍 Like the video 🔔 Subscribe to the channel 📬 Get All Source Code, Labs & Exercises https://learnwithdevopsengineer.beehiiv.com/subscribe DevOps HomeLab Series - https://youtu.be/a2XtV0piToU DevOps in Production: Real-World Simulations - https://www.youtube.com/playlist?list=PLC3q1iUHNvtV7dxVrkJngsapxabQDEEmw DevOps Home Lab Bootcamp – Build Real Skills at Home - https://www.youtube.com/playlist?list=PLC3q1iUHNvtVHt9QXnWSD3anLZY63PTkt DevOps in Production: Real-World Simulations - https://www.youtube.com/playlist?list=PLC3q1iUHNvtV7dxVrkJngsapxabQDEEmw DevOps in Action: Real Incidents, Debugging & Automation - https://www.youtube.com/playlist?list=PLC3q1iUHNvtVNTF5W_uvFUFQBvwUqxMjv Github Repo - https://github.com/learnwithdevopsengineer3682/devops-homelab-series/ 📌 Like + comment to support this series ❤️ 💛 Support my work: https://www.buymeacoffee.com/learnwithdevopsengineer