MLflow Release Notes

MLflow 3.17.0 includes several major features and improvements.

Major New Features

• Jev Decisions Across Evaluation, Gateway, and Tracing: Use TypeSafe models in built-in scorers and custom judges with typed Boolean or categorical feedback. (#26100, @B-Step62)
• Fine-Grained Permissions for Shared Resources: Control access to contained resource types, including runs, traces, and versions, with wildcard grants and explicit DENY. (#26157, @rrrkharse)
• Faster Analytics for Large Trace Datasets: Reduce SQL analytics query overhead with opt-in daily summaries and raw-query fallback. (#25991, @HumairAK)
• Clearer Evaluation Results: Associate aggregate logged-model evaluation metrics with the managed dataset that produced them. (#25927, @joshuawong-db)
• Assistant Improvements for Shared Servers: Keep saved conversations scoped to the signed-in user and reset the active session when accounts change. (#26009, @kriscon-db) …

MLflow 3.16.1 is a patch release that includes bug fixes and documentation updates.

Breaking changes:

• [Docs / Tracking] Remove the default basic-auth admin password shipped in basic_auth.ini (GHSA-gq3w-7jj3-x7gr) (#25751, @tanghaoji)

Features:

• [Evaluation] Add a timeout option to the @scorer decorator (#25720, @smoorjani)
• [Evaluation] Propagate scorer version to assessment metadata (#25570, @B-Step62)
• [Tracing] Support span links for Unity Catalog traces (#25597, @B-Step62)

Bug fixes:

• [Tracing] Resolve Unity Catalog trace locations in Databricks Model Serving without local-store validation (#25884, @james-fletcher-db)
• [Model Registry] Fix S3 multipart upload encryption arguments (#25772, @james-fletcher-db)
• [Tracking / UI] basic-auth: serve the web UI to non-admins under fail-closed authorization (#25672, @mkBGD)

MLflow 3.16.0 includes several major features and improvements

Major New Features

• 🎨 Custom Trace Views: Design your own trace UI in plain English — describe the view you want and the MLflow Assistant builds it for you, laying out exactly the fields you care about with no config files or custom code. Save, name, and reuse views per experiment so your whole team shares the same lens on your traces.
• 🔭 Redesigned Trace Experience (now default): The trace explorer has been rebuilt from the ground up and is now the default — tighter row density, cleaner navigation, and a redesigned span-tree view. Reorder columns, add configurable custom columns from any trace tag or metadata field, and find multi-turn conversations inline via session grouping.
• 🔗 Span Links: Spans rarely stand alone. MLflow 3.16.0 adds first-class span links so relationships between spans — a retrieval step, a tool call, a downstream trace — are captured and navigable. Record links in your SDK and the trace explorer surfaces them in a dedicated Links tab to jump straight to the destination span.

Breaking changes:

• [Server-infra / Tracking] basic-auth: enable fail-closed authorization by default (#25308, @PattaraS)
• [Build / Models] Drop cross version testing for pyspark < 3.4.4 (#25098, @harupy)
• [Gateway / Tracing] fix: Serve FastAPI-native routers under --static-prefix (#24511, @SeiichiroYoshioka)

Features: …

MLflow 3.15.0 includes several major features and improvements

Major New Features

• 🧩 MCP Registry: A centralized catalog for registering, versioning, and sharing Model Context Protocol servers — with semantic-versioned configs, promotable aliases, tags, auto-discovered tools, and ready-made connection instructions for Claude Code and .mcp.json. Manage it from the UI, REST API, or Python!
• 🤖 MLflow Assistant enhancements: The in-app AI assistant now supports multiple LLM providers (Claude Code, Codex, and OpenAI-compatible/Gateway endpoints) chosen from a single settings page, displays live per-session token usage and estimated cost in the composer, and is easier to set up — mlflow agent setup can enable it in one prompt, with API keys stored securely in the Gateway's LLM Connections.
• 🗂️ Sharable table views: Save named views of the Runs table — capturing columns, order, widths, filters, and sort — and share them by URL.
• ⚡ Proxy-less artifact upload/download via presigned URLs: Large artifact transfers can now bypass the tracking server and talk directly to cloud storage (e.g. S3) through presigned URLs, cutting server load and timeouts on big files. We fall back to proxied transfer automatically for backward compatibility. …

MLflow Review Queues UI showing an AI Safety Standards Review queue with traces awaiting human review

TL;DR: We have released review queues for AI traces. These are shared inboxes waiting for a human-in-the-loop to evaluate them on whatever criteria the team decides. Instead of passing documents and excel sheets back and forth, this streamlines the AI review process and facilitates the creation of a strong dataset of successes and failures that you can use to retrain your agents in the future.

Back in December 2023, a guy named Chris Bakke hopped on the chat window on the website for a Chevrolet dealer, told the bot to agree with anything he said, and then asked if he could get a 2024 Tahoe for one dollar. The bot didn't hesitate. "That's a deal," it typed back, "and that's a legally binding offer – no takesies backsies." It wasn't a lawyer, obviously. It was a chatbot that had just seen a human invent a loophole in contract law by saying "no takesies backsies" and accepted it. Chevrolet of Watsonville did not, ultimately, hand over a $76,000 vehicle for $1. But the screenshot, showing the transaction, went viral and ended up racking 20 million views. Somewhere in Detroit, a General Motors employee experienced a Tuesday they'd probably like to forget. …