Building a Production-Ready ML Monitoring Stack

Description

Visualizing model performance is key to debugging and optimization. This project demonstrates how to integrate industrial-strength monitoring tools directly into your training loop. Key Features: 🔹 Automated Logging: Custom callbacks logging directly to ml_system.log . 🔹 Metric Exporting: Real-time Gauge updates for Loss and Accuracy via prometheus_client. 🔹 Visual Analytics: Interactive TensorBoard dashboard for deep-dive analysis. Check out the code to see how easy it is to add professional observability to your next AI project! #AI #ArtificialIntelligence #MLMonitoring #DevOps #Keras #PythonProgramming #Dashboard #Engineering #MLOps, #TensorFlow #Tutorial, #Keras Callbacks, #Prometheus Monitoring, TensorBoard Dashboard, MNIST Python, Deep Learning Observability, Real-time ML Metrics, Python Logging, Machine Learning Infrastructure