Managed MLflow

Capture inputs, outputs, and step-by-step execution—including prompts, retrievals, and tool calls—with MLflow’s open-source, OpenTelemetry-compatible tracing. Automatically instrument popular GenAI libraries or ingest traces directly. Debug and iterate faster with interactive timeline views, side-by-side comparisons, and zero vendor lock-in.

Evaluate GenAI agents using LLM-as-a-judge and human feedback—right in the MLflow UI. Build datasets from production traces, compare outputs across versions, and assess quality with prebuilt or custom metrics like hallucination or relevance. Incorporate expert feedback via web UIs or app APIs to align with human judgment and continuously improve results.

Version prompts, agents, and application code in one place with MLflow. Link traces, evaluations, and performance data to specific versions for full lifecycle lineage. Reuse and compare prompts across workflows, manage agent versions with associated metrics and parameters, and integrate with Git and CI/CD to accelerate governed iteration.

Monitor GenAI quality in real time with MLflow’s dashboards, trace explorers, and automated alerts. Track issues like PII leakage, latency spikes, or unhelpful responses using LLM-judge evaluations and custom metrics. Configure online evaluations and act quickly—before users are affected.

Automatically track parameters, metrics, artifacts, and models from any ML or deep learning framework. MLflow gives you a complete audit trail and supports deep comparisons across architectures, checkpoints, and training workflows—at scale.
 

Automatically log built-in and custom metrics for tasks like classification or regression. Compare results against baselines, log artifacts like ROC curves, and validate models on new datasets—before they reach production.

Discover, share, and manage models centrally with the MLflow Model Registry—integrated with Unity Catalog for end-to-end governance. Track deployment status and collaborate across teams with full visibility into model performance across environments

Deploy models with a reproducible packaging format that includes all code, dependencies, and weights. Serve them as REST APIs or run high-throughput batch inference with ai_query—optimized for both CPU and GPU via Mosaic AI Model Serving.