Apr 5
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Monitoring is one of the most critical (and often ignored) parts of MLOps. You can build great models, but without proper monitoring, things break silently in production. Github Link: https://github.com/Mayurji/MLOps-Project/tree/main/real-time-fraud-detection In this video, we cover how to monitor both infrastructure and machine learning models using Prometheus, Grafana, and Evidently. You’ll learn how to track system health, model performance, and data drift—so your ML systems stay reliable in the real world. 🧠 What you’ll learn: • Setting up Prometheus for infrastructure metrics • Visualizing metrics using Grafana dashboards • Monitoring ML models using Evidently • Detecting data drift and model performance issues • Building a complete monitoring stack for ML systems 💻 Who is this for? ML Engineers Data Scientists MLOps Engineers Developers deploying ML models 📌 By the end of this video, you’ll understand how to build a robust monitoring system for both your infrastructure and machine learning models. 👍 Like the video if you found it useful 💬 Comment your questions or topics you want next 🔔 Subscribe for more deep dives on ML, AI, and MLOps #MLOps #Prometheus #Grafana #Evidently #MachineLearning #AIEngineering #DataScience #Monitoring #MLSystems