MLflow

MLflow — Technical Use Cases

• LLM Application Tracing: Instrument LLM apps and multi-step agents with OpenTelemetry-based tracing to capture latency, token usage, and intermediate outputs across every span — enabling root-cause analysis in production.
• Agent Evaluation Pipelines: Run systematic evaluations of LLM agents using 50+ built-in metrics and LLM-judge patterns; integrate evaluation steps into CI/CD workflows via the MLflow API.
• Prompt Versioning & Optimization: Manage prompt templates as versioned artifacts, run A/B comparisons, and feed results into optimization loops — all tracked within the MLflow experiment store.
• Multi-Provider AI Gateway: Deploy the MLflow AI Gateway to unify authentication, routing, and rate-limit handling across multiple LLM providers, decoupling application code from provider-specific SDKs.
• ML Experiment Tracking: Log hyperparameters, metrics, and model artifacts from PyTorch training runs; compare runs in the MLflow UI and promote best-performing models to the registry.
• Model Registry & Deployment: Version and stage ML models through the model registry, then serve them via the MLflow model serving interface or export for downstream deployment pipelines.
• Observability Stack Integration: Leverage native OpenTelemetry support to pipe MLflow trace data into existing observability platforms, maintaining a single pane of glass across AI and infrastructure telemetry.