Production Observability and Monitoring for LLM Apps using MLflow in Flask ChatBot (Autologging)

Description

This video demonstrates how to add real observability and monitoring to an LLM application using MLflow. Instead of a toy example, we take a working Flask + OpenAI chatbot and enable MLflow OpenAI autologging to capture traces, latency, token usage, and request details automatically. This is exactly how observability works in real LLM applications. In this video, you will learn: • How to enable MLflow OpenAI autologging • How MLflow captures LLM traces automatically • How to monitor latency and token usage • How to debug LLM behavior using traces • How observability works in real AI applications This video is part of my complete MLflow playlist where I cover MLflow from basics to real production workflows. If you are working with LLMs, observability is not optional — it is essential. GitHub code: https://github.com/datageekrj/flask-chatbot-mlflow-observability MLflow playlist: (add playlist link) Subscribe for practical ML, MLOps, and LLM engineering content.