Bump mlflow from 2.22.2 to 3.9.0rc0 in /nginx/volumes/data/icarus/visuals/trained_models/mlruns/2/d897c95d7ab74ebb991c90b49134ae63/artifacts/model

[dependabot]

Bumps mlflow from 2.22.2 to 3.9.0rc0.

Release notes

Sourced from mlflow's releases.

v3.9.0rc0

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

• 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
• 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
• ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
• 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
• 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
• 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
• 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

Stay tuned for the full release, which will be packed with even more features and bugfixes.

To try out this release candidate, please run:

pip install mlflow==3.9.0rc0

Please try it out and report any issues on the issue tracker.

v3.8.1

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

• [Tracking] Skip registering sqlalchemy store when sqlalchemy lib is not installed (#19563, @​WeichenXu123)
• [Models / Scoring] fix(security): prevent command injection via malicious model artifacts (#19583, @​ColeMurray)
• [Prompts] Fix prompt registration with model_config on Databricks (#19617, @​TomeHirata)
• [UI] Fix UI blank page on plain HTTP by replacing crypto.randomUUID with uuid library (#19644, @​copilot-swe-agent)

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

v3.8.0

MLflow 3.8.0 includes several major features and improvements

Major Features

• ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
• ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
• ⚖️ DeepEval and RAGAS Judges Integration: New get_judge API enables using DeepEval and RAGAS evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani, #19345, @​SomtochiUmeh)
• 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
• ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

• Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Changelog

Sourced from mlflow's changelog.

3.9.0rc0 (2026-01-15)

We're excited to announce MLflow 3.9.0rc0, a pre-release including several notable updates:

Major New Features:

• 🔮 MLflow Assistant: Figuring out the next steps to debug your apps and agents can be challenging. We're excited to introduce the MLflow Assistant, an in-product chatbot that can help you identify, diagnose, and fix issues. The assistant is backed by Claude Code, and directly passes context from the MLflow UI to Claude. Click on the floating "Assistant" button in the bottom right of the MLflow UI to get started!
• 📈 Trace Overview Dashboard: You can now get insights into your agent's performance at a glance with the new "Overview" tab in GenAI experiments. Many pre-built statistics are available out of the box, including performance metrics (e.g. latency, request count), quality metrics (based on assessments), and tool call summaries. If there are any additional charts you'd like to see, please feel free to raise an issue in the MLflow repository!
• ✨ AI Gateway: We're revamping our AI Gateway feature! AI Gateway provides a unified interface for your API requests, allowing you to route queries to your LLM provider(s) of choice. In MLflow 3.9.0rc0, the Gateway server is now located directly in the tracking server, so you don't need to spin up a new process. Additional features such as passthrough endpoints, traffic splits, and fallback models are also available, with more to come soon! For more detailed information, please take a look at the docs.
• 🔎 Online Monitoring with LLM Judges: Configure LLM judges to automatically run on your traces, without having to write a line of code! You can either use one of our pre-defined judges, or provide your own prompt and instructions to create custom metrics. Head to the new "Judges" tab within the GenAI Experiment UI to get started.
• 🤖 Judge Builder UI: Define and iterate on custom LLM judge prompts directly from the UI! Within the new "Judges" tab, you can create your own prompt for an LLM judge, and test-run it on your traces to see what the output would be. Once you're happy with it, you can either use it for online monitoring (as mentioned above), or use it via the Python SDK for your evals.
• 🔗 Distributed Tracing: Trace context can now be propagated across different services and processes, allowing you to truly track request lifecycles from end to end. The related APIs are defined in the mlflow.tracing.distributed module (with more documentation to come soon).
• 📚 MemAlign - a new judge optimizer algorithm: We're excited to introduce MemAlignOptimizer, a new algorithm that makes your judges smarter over time. It learns general guidelines from past feedback while dynamically retrieving relevant examples at runtime, giving you more accurate evaluations.

Stay tuned for the full release, which will be packed with even more features and bugfixes.

To try out this release candidate, please run:

pip install mlflow==3.9.0rc0

3.8.1 (2025-12-26)

MLflow 3.8.1 includes several bug fixes and documentation updates.

Bug fixes:

• [Tracking] Skip registering sqlalchemy store when sqlalchemy lib is not installed (#19563, @​WeichenXu123)
• [Models / Scoring] fix(security): prevent command injection via malicious model artifacts (#19583, @​ColeMurray)
• [Prompts] Fix prompt registration with model_config on Databricks (#19617, @​TomeHirata)
• [UI] Fix UI blank page on plain HTTP by replacing crypto.randomUUID with uuid library (#19644, @​copilot-swe-agent)

Small bug fixes and documentation updates:

#19539, #19451, #19409, @​smoorjani; #19493, @​alkispoly-db

3.8.0 (2025-12-19)

MLflow 3.8.0 includes several major features and improvements

Major Features

• ⚙️ Prompt Model Configuration: Prompts can now include model configuration, allowing you to associate specific model settings with prompt templates for more reproducible LLM workflows. (#18963, #19174, #19279, @​chenmoneygithub)
• ⏳ In-Progress Trace Display: The Traces UI now supports displaying spans from in-progress traces with auto-polling, enabling real-time debugging and monitoring of long-running LLM applications. (#19265, @​B-Step62)
• ⚖️ DeepEval Judges Integration: New get_judge API enables using DeepEval's evaluation metrics as MLflow scorers, providing access to 20+ evaluation metrics including answer relevancy, faithfulness, and hallucination detection. (#18988, @​smoorjani)
• 🛡️ Conversational Safety Scorer: New built-in scorer for evaluating safety of multi-turn conversations, analyzing entire conversation histories for hate speech, harassment, violence, and other safety concerns. (#19106, @​joelrobin18)
• ⚡ Conversational Tool Call Efficiency Scorer: New built-in scorer for evaluating tool call efficiency in multi-turn agent interactions, detecting redundant calls, missing batching opportunities, and poor tool selections. (#19245, @​joelrobin18)

Important Notice

• Collection of UI Telemetry. From MLflow 3.8.0 onwards, MLflow will collect anonymized data about UI interactions, similar to the telemetry we collect for the Python SDK. If you manage your own server, UI telemetry is automatically disabled by setting the existing environment variables: MLFLOW_DISABLE_TELEMETRY=true or DO_NOT_TRACK=true. If you do not manage your own server (e.g. you use a managed service or are not the admin), you can still opt out personally via the new "Settings" tab in the MLflow UI. For more information, please read the documentation on usage tracking.

... (truncated)

Commits

• d8c9d8c Run python3 dev/update_mlflow_versions.py pre-release ... (#20018)
• e65f119 Run python3 dev/update_ml_package_versions.py (#20013)
• 0692445 Move DevTools section from PR description to separate comment (#20012)
• 089d869 Enhance LiteLLMConfig validation and normalize Databricks base URL (#20008)
• df01edd Implement telemetry tracking for gateway invocations and CRUD operations (#19...
• 99b5a3d Merge MLflow Assistant branch (#20011)
• a58d7d7 Remove vertical line (#20006)
• f4db4c3 Remove over time label (#19989)
• aba7094 Distinguish local remote server (#19893)
• 3bc8482 Add coming soon components (#20005)
• Additional commits viewable in compare view

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