One post tagged with "mlflow"

As LLM-powered applications grow in complexity, observability becomes essential. You need to understand what your application is doing — what prompts are being sent, what responses come back, how long each call takes, and how many tokens are consumed. MLflow provides a powerful tracing framework that captures all of this, which can be integrated with ogx for observability.

In this post, we'll walk through two approaches for exporting OGX traces into MLflow:

1. MLflow SDK — Direct instrumentation using MLflow's built-in tracing and autologging
2. OTel Collector — Decoupled telemetry pipeline using OpenTelemetry auto-instrumentation and an OTel Collector as the intermediary

By the end, you'll understand when to use each approach and how to set them up.