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Bump the pip group across 10 directories with 1 update #34

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Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/0f09b45dcd764f7780eff72819b39508/artifacts/Female_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/3c5c75c38ad142098bc902c6af674a01/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/3cbb220738a94697bc1d08cf782ad7c5/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/45681032341f4e5692822c55f8146b52/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/8a7ac7a7f14844f093e986320bde5e64/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/8ca8002168f345d4bb07718473a7f647/artifacts/Male_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/a11e707aace849a38eacde5213870bf4/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/ae0f1b850e45436baebfb57cac7e286d/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/f25d4b0464f74b4e80c140a09a8a61c2/artifacts/Combined_Model directory: mlflow.
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/f6c75b4fbc7543a4bc3392afe6d3629b/artifacts/Combined_Model directory: mlflow.

Updates mlflow from 2.21.0 to 3.1.0

Release notes

Sourced from mlflow's releases.

3️⃣ MLflow 3 3️⃣

MLflow 3: Redefining MLOps for the GenAI Era

MLflow 3 is now available to everyone, marking the biggest evolution in the best open-source MLOps platform's history and transforming how millions of developers build, deploy, AI applications. While previous versions focused on traditional ML workflows, MLflow 3 fundamentally reimagines the platform for the GenAI era. This isn't just an update, but a complete paradigm shift that brings enterprise-grade GenAI capabilities to the open source community for the first time.

🎯 Improved Model Tracking for GenAI

MLflow 3 introduces a refined architecture with the new LoggedModel entity as a first-class citizen, moving beyond the traditional run-centric approach. This enables better organization and comparison of GenAI models. agents, deep learning checkpoints, and model variants across experiments.

🔗 Comprehensive Performance Tracking & Observability

Enhanced model tracking provides comprehensive lineage between models, runs, traces, prompts, and evaluation metrics. The new model-centric design allows you to group traces and metrics from different development environments and production, enabling rich comparisons across model versions.

📊 Production-Grade GenAI Evaluation

MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle. From development through production, use the same quality scorers to ensure your applications deliver accurate, reliable responses while managing cost and latency. Visit documentation for more details.

👥 Human-in-the-Loop Feedback

Real-world GenAI applications need human oversight. MLflow 3 now tracks human annotations and feedback for model predictions, enabling streamlined human-in-the-loop evaluation cycles. This creates a collaborative environment where data scientists, domain experts, and stakeholders can efficiently improve model quality together. (Note: Currently available in Databricks Managed MLflow. Open source release coming in the next few months.)

⚡️ State-of-the-Art Prompt Optimization

Transform prompt engineering from art to science. The MLflow Prompt Registry now includes prompt optimization capabilities built on top of the state-of-the-art research, allowing you to automatically improve prompts using evaluation feedback and labeled datasets. This includes versioning, tracking, and systematic prompt engineering workflows.

📚 Revamped Website and Documentation

The MLflow documentation and website has been fully redesigned to support two main user journeys: GenAI development and classic machine learning workflows. The new structure offers dedicated sections for GenAI features (including LLMs, prompt engineering, and tracing), and traditional ML capabilities such as experiment tracking, model registry, deployment, and evaluation.

▶︎▶︎▶︎ Ready to Get Started?　▶︎▶︎▶︎

Get up and running with MLflow 3 in minutes:

pip install 'mlflow>=3.1'

Resources:

🌐 New Website | 📖 Documentation | 🎉:Release Notes

🏎️ The Road Ahead 🏎️

It is just the beginning. The open source community continues driving innovation toward the world's best open-source MLOps/LLMOps platform. Here's how you can be part of the journey:

How to Get Involved:

  • 🔧 Contribute Code: From bug fixes to major features, all contributions welcome
  • 🐝 Report Issues: Help us improve by reporting bugs and requesting features
  • 💬 Join Discussions: Technical discussions, roadmap planning, and peer support
  • 📝 Share Your Story: Write blogs, tutorials, and docs about your MLflow implementations to help others learn!

... (truncated)

Changelog

Sourced from mlflow's changelog.

CHANGELOG

3.1 (2025-06-11)

MLflow 3 includes several major features and improvements

Features:

Bug fixes:

Breaking changes:

  • [Tracking] Move prompt registry APIs under mlflow.genai.prompts namespace (#16174, @​B-Step62)
  • [Model Registry] Default URI to databricks-uc when tracking URI is databricks & registry URI is unspecified (#16135, @​dbczumar)
  • [Tracking] Do not log SHAP explainer in mlflow.evaluate (#15827, @​harupy)
  • [Tracking] Update DataFrame schema returned from mlflow.search_trace() to be V3 format (#15643, @​B-Step62)

... (truncated)

Commits
  • 39a419b Run python3 dev/update_mlflow_versions.py pre-release ... (#16187)
  • c44e13f Run python3 dev/update_ml_package_versions.py (#16186)
  • 43b4091 Mlflow 3 docs refactor (#15954)
  • 4b74195 Remove log_models from openai autolog (#16178)
  • 3d6758f Fix labeling schemas usage (#16177)
  • 23595f4 [BUG] ERROR mlflow.server: Exception on /graphql when trying to open a run if...
  • 6464d1b Link prompts to traces when loaded via fluent API (#16167)
  • 5795d77 Update ML package versions for 3.1.0 (#16171)
  • a61080e Correct the way to check the error messages for optuna study (#16169)
  • e1dfdeb Move prompt registry APIs under mlflow.genai.prompt namespace (#16174)
  • Additional commits viewable in compare view

Updates mlflow from 2.21.0 to 3.1.0

Release notes

Sourced from mlflow's releases.

3️⃣ MLflow 3 3️⃣

MLflow 3: Redefining MLOps for the GenAI Era

MLflow 3 is now available to everyone, marking the biggest evolution in the best open-source MLOps platform's history and transforming how millions of developers build, deploy, AI applications. While previous versions focused on traditional ML workflows, MLflow 3 fundamentally reimagines the platform for the GenAI era. This isn't just an update, but a complete paradigm shift that brings enterprise-grade GenAI capabilities to the open source community for the first time.

🎯 Improved Model Tracking for GenAI

MLflow 3 introduces a refined architecture with the new LoggedModel entity as a first-class citizen, moving beyond the traditional run-centric approach. This enables better organization and comparison of GenAI models. agents, deep learning checkpoints, and model variants across experiments.

🔗 Comprehensive Performance Tracking & Observability

Enhanced model tracking provides comprehensive lineage between models, runs, traces, prompts, and evaluation metrics. The new model-centric design allows you to group traces and metrics from different development environments and production, enabling rich comparisons across model versions.

📊 Production-Grade GenAI Evaluation

MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle. From development through production, use the same quality scorers to ensure your applications deliver accurate, reliable responses while managing cost and latency. Visit documentation for more details.

👥 Human-in-the-Loop Feedback

Real-world GenAI applications need human oversight. MLflow 3 now tracks human annotations and feedback for model predictions, enabling streamlined human-in-the-loop evaluation cycles. This creates a collaborative environment where data scientists, domain experts, and stakeholders can efficiently improve model quality together. (Note: Currently available in Databricks Managed MLflow. Open source release coming in the next few months.)

⚡️ State-of-the-Art Prompt Optimization

Transform prompt engineering from art to science. The MLflow Prompt Registry now includes prompt optimization capabilities built on top of the state-of-the-art research, allowing you to automatically improve prompts using evaluation feedback and labeled datasets. This includes versioning, tracking, and systematic prompt engineering workflows.

📚 Revamped Website and Documentation

The MLflow documentation and website has been fully redesigned to support two main user journeys: GenAI development and classic machine learning workflows. The new structure offers dedicated sections for GenAI features (including LLMs, prompt engineering, and tracing), and traditional ML capabilities such as experiment tracking, model registry, deployment, and evaluation.

▶︎▶︎▶︎ Ready to Get Started?　▶︎▶︎▶︎

Get up and running with MLflow 3 in minutes:

pip install 'mlflow>=3.1'

Resources:

🌐 New Website | 📖 Documentation | 🎉:Release Notes

🏎️ The Road Ahead 🏎️

It is just the beginning. The open source community continues driving innovation toward the world's best open-source MLOps/LLMOps platform. Here's how you can be part of the journey:

How to Get Involved:

  • 🔧 Contribute Code: From bug fixes to major features, all contributions welcome
  • 🐝 Report Issues: Help us improve by reporting bugs and requesting features
  • 💬 Join Discussions: Technical discussions, roadmap planning, and peer support
  • 📝 Share Your Story: Write blogs, tutorials, and docs about your MLflow implementations to help others learn!

... (truncated)

Changelog

Sourced from mlflow's changelog.

CHANGELOG

3.1 (2025-06-11)

MLflow 3 includes several major features and improvements

Features:

Bug fixes:

Breaking changes:

  • [Tracking] Move prompt registry APIs under mlflow.genai.prompts namespace (#16174, @​B-Step62)
  • [Model Registry] Default URI to databricks-uc when tracking URI is databricks & registry URI is unspecified (#16135, @​dbczumar)
  • [Tracking] Do not log SHAP explainer in mlflow.evaluate (#15827, @​harupy)
  • [Tracking] Update DataFrame schema returned from mlflow.search_trace() to be V3 format (#15643, @​B-Step62)

... (truncated)

Commits
  • 39a419b Run python3 dev/update_mlflow_versions.py pre-release ... (#16187)
  • c44e13f Run python3 dev/update_ml_package_versions.py (#16186)
  • 43b4091 Mlflow 3 docs refactor (#15954)
  • 4b74195 Remove log_models from openai autolog (#16178)
  • 3d6758f Fix labeling schemas usage (#16177)
  • 23595f4 [BUG] ERROR mlflow.server: Exception on /graphql when trying to open a run if...
  • 6464d1b Link prompts to traces when loaded via fluent API (#16167)
  • 5795d77 Update ML package versions for 3.1.0 (#16171)
  • a61080e Correct the way to check the error messages for optuna study (#16169)
  • e1dfdeb Move prompt registry APIs under mlflow.genai.prompt namespace (#16174)
  • Additional commits viewable in compare view

Updates mlflow from 2.21.0 to 3.1.0

Release notes

Sourced from mlflow's releases.

3️⃣ MLflow 3 3️⃣

MLflow 3: Redefining MLOps for the GenAI Era

MLflow 3 is now available to everyone, marking the biggest evolution in the best open-source MLOps platform's history and transforming how millions of developers build, deploy, AI applications. While previous versions focused on traditional ML workflows, MLflow 3 fundamentally reimagines the platform for the GenAI era. This isn't just an update, but a complete paradigm shift that brings enterprise-grade GenAI capabilities to the open source community for the first time.

🎯 Improved Model Tracking for GenAI

MLflow 3 introduces a refined architecture with the new LoggedModel entity as a first-class citizen, moving beyond the traditional run-centric approach. This enables better organization and comparison of GenAI models. agents, deep learning checkpoints, and model variants across experiments.

🔗 Comprehensive Performance Tracking & Observability

Enhanced model tracking provides comprehensive lineage between models, runs, traces, prompts, and evaluation metrics. The new model-centric design allows you to group traces and metrics from different development environments and production, enabling rich comparisons across model versions.

📊 Production-Grade GenAI Evaluation

MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle. From development through production, use the same quality scorers to ensure your applications deliver accurate, reliable responses while managing cost and latency. Visit documentation for more details.

👥 Human-in-the-Loop Feedback

Real-world GenAI applications need human oversight. MLflow 3 now tracks human annotations and feedback for model predictions, enabling streamlined human-in-the-loop evaluation cycles. This creates a collaborative environment where data scientists, domain experts, and stakeholders can efficiently improve model quality together. (Note: Currently available in Databricks Managed MLflow. Open source release coming in the next few months.)

⚡️ State-of-the-Art Prompt Optimization

Transform prompt engineering from art to science. The MLflow Prompt Registry now includes prompt optimization capabilities built on top of the state-of-the-art research, allowing you to automatically improve prompts using evaluation feedback and labeled datasets. This includes versioning, tracking, and systematic prompt engineering workflows.

📚 Revamped Website and Documentation

The MLflow documentation and website has been fully redesigned to support two main user journeys: GenAI development and classic machine learning workflows. The new structure offers dedicated sections for GenAI features (including LLMs, prompt engineering, and tracing), and traditional ML capabilities such as experiment tracking, model registry, deployment, and evaluation.

▶︎▶︎▶︎ Ready to Get Started?　▶︎▶︎▶︎

Get up and running with MLflow 3 in minutes:

pip install 'mlflow>=3.1'

Resources:

🌐 New Website | 📖 Documentation | 🎉:Release Notes

🏎️ The Road Ahead 🏎️

It is just the beginning. The open source community continues driving innovation toward the world's best open-source MLOps/LLMOps platform. Here's how you can be part of the journey:

How to Get Involved:

  • 🔧 Contribute Code: From bug fixes to major features, all contributions welcome
  • 🐝 Report Issues: Help us improve by reporting bugs and requesting features
  • 💬 Join Discussions: Technical discussions, roadmap planning, and peer support
  • 📝 Share Your Story: Write blogs, tutorials, and docs about your MLflow implementations to help others learn!

... (truncated)

Changelog

Sourced from mlflow's changelog.

CHANGELOG

3.1 (2025-06-11)

MLflow 3 includes several major features and improvements

Features:

Bug fixes:

Breaking changes:

  • [Tracking] Move prompt registry APIs under mlflow.genai.prompts namespace (#16174, @​B-Step62)
  • [Model Registry] Default URI to databricks-uc when tracking URI is databricks & registry URI is unspecified (#16135, @​dbczumar)
  • [Tracking] Do not log SHAP explainer in mlflow.evaluate (#15827, @​harupy)
  • [Tracking] Update DataFrame schema returned from mlflow.search_trace() to be V3 format (#15643, @​B-Step62)

... (truncated)

Commits
  • 39a419b Run python3 dev/update_mlflow_versions.py pre-release ... (#16187)
  • c44e13f Run python3 dev/update_ml_package_versions.py (#16186)
  • 43b4091 Mlflow 3 docs refactor (#15954)
  • 4b74195 Remove log_models from openai autolog (#16178)
  • 3d6758f Fix labeling schemas usage (#16177)
  • 23595f4 [BUG] ERROR mlflow.server: Exception on /graphql when trying to open a run if...
  • 6464d1b Link prompts to traces when loaded via fluent API (#16167)
  • 5795d77 Update ML package versions for 3.1.0 (#16171)
  • a61080e Correct the way to check the error messages for optuna study (#16169)
  • e1dfdeb Move prompt registry APIs under mlflow.genai.prompt namespace (#16174)
  • Additional commits viewable in compare view

Updates mlflow from 2.21.0 to 3.1.0

Release notes

Sourced from mlflow's releases.

3️⃣ MLflow 3 3️⃣

MLflow 3: Redefining MLOps for the GenAI Era

MLflow 3 is now available to everyone, marking the biggest evolution in the best open-source MLOps platform's history and transforming how millions of developers build, deploy, AI applications. While previous versions focused on traditional ML workflows, MLflow 3 fundamentally reimagines the platform for the GenAI era. This isn't just an update, but a complete paradigm shift that brings enterprise-grade GenAI capabilities to the open source community for the first time.

🎯 Improved Model Tracking for GenAI

MLflow 3 introduces a refined architecture with the new LoggedModel entity as a first-class citizen, moving beyond the traditional run-centric approach. This enables better organization and comparison of GenAI models. agents, deep learning checkpoints, and model variants across experiments.

🔗 Comprehensive Performance Tracking & Observability

Enhanced model tracking provides comprehensive lineage between models, runs, traces, prompts, and evaluation metrics. The new model-centric design allows you to group traces and metrics from different development environments and production, enabling rich comparisons across model versions.

📊 Production-Grade GenAI Evaluation

MLflow's evaluation and monitoring capabilities help you systematically measure, improve, and maintain the quality of your GenAI applications throughout their lifecycle. From development through production, use the same quality scorers to ensure your applications deliver accurate, reliable responses while managing cost and latency. Visit documentation for more details.

👥 Human-in-the-Loop Feedback

Real-world GenAI applications need human oversight. MLflow 3 now tracks human annotations and feedback for model predictions, enabling streamlined human-in-the-loop evaluation cycles. This creates a collaborative environment where data scientists, domain experts, and stakeholders can efficiently improve model quality together. (Note: Currently available in Databricks Managed MLflow. Open source release coming in the next few months.)

⚡️ State-of-the-Art Prompt Optimization

Transform prompt engineering from art to science. The MLflow Prompt Registry now includes prompt optimization capabilities built on top of the state-of-the-art research, allowing you to automatically improve prompts using evaluation feedback and labeled datasets. This includes versioning, tracking, and systematic prompt engineering workflows.

📚 Revamped Website and Documentation

The MLflow documentation and website has been fully redesigned to support two main user journeys: GenAI development and classic machine learning workflows. The new structure offers dedicated sections for GenAI features (including LLMs, prompt engineering, and tracing), and traditional ML capabilities such as experiment tracking, model registry, deployment, and evaluation.

▶︎▶︎▶︎ Ready to Get Started?　▶︎▶︎▶︎

Get up and running with MLflow 3 in minutes:

pip install 'mlflow>=3.1'

Resources:

🌐 New Website | 📖 Documentation | 🎉:Release Notes

🏎️ The Road Ahead 🏎️

It is just the beginning. The open source community continues driving innovation toward the world's best open-source MLOps/LLMOps platform. Here's how you can be part of the journey:

How to Get Involved:

  • 🔧 Contribute Code: From bug fixes to major features, all contributions welcome
  • 🐝 Report Issues: Help us improve by reporting bugs and requesting features
  • 💬 Join Discussions: Technical discussions, roadmap planning, and peer support
  • 📝 Share Your Story: Write blogs, tutorials, and docs about your MLflow implementations to help others learn!

... (truncated)

Changelog

Sourced from mlflow's changelog.

CHANGELOG

3.1 (2025-06-11)

MLflow 3 includes several major features and improvements

Features:

Bug fixes:

Breaking changes:

  • [Tracking] Move prompt registry APIs under mlflow.genai.prompts namespace (#16174, @​B-Step62)
  • [Model Registry] Default URI to databricks-uc when tracking URI is databricks & registry URI is unspecified (#16135, @​dbczumar)
  • [Tracking] Do not log SHAP explainer in mlflow.evaluate (#15827, @​harupy)
  • [Tracking] Update DataFrame schema returned from mlflow.search_trace() to be V3 format (#15643, @​B-Step62)

... (truncated)

Commits

@dependabot
Bump the pip group across 10 directories with 1 update
d52c243 
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/0f09b45dcd764f7780eff72819b39508/artifacts/Female_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/3c5c75c38ad142098bc902c6af674a01/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/3cbb220738a94697bc1d08cf782ad7c5/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/45681032341f4e5692822c55f8146b52/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/8a7ac7a7f14844f093e986320bde5e64/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/8ca8002168f345d4bb07718473a7f647/artifacts/Male_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/a11e707aace849a38eacde5213870bf4/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/ae0f1b850e45436baebfb57cac7e286d/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/f25d4b0464f74b4e80c140a09a8a61c2/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).
Bumps the pip group with 1 update in the /Project/mlops/mlruns/377601592381563769/f6c75b4fbc7543a4bc3392afe6d3629b/artifacts/Combined_Model directory: [mlflow](https://github.com/mlflow/mlflow).


Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

Updates `mlflow` from 2.21.0 to 3.1.0
- [Release notes](https://github.com/mlflow/mlflow/releases)
- [Changelog](https://github.com/mlflow/mlflow/blob/master/CHANGELOG.md)
- [Commits](mlflow/mlflow@v2.21.0...v3.1.0)

---
updated-dependencies:
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: mlflow
  dependency-version: 3.1.0
  dependency-type: direct:production
  dependency-group: pip
...

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dependabot bot commented on behalf of github Jun 24, 2025

Superseded by #35.

@dependabot dependabot bot closed this Jun 24, 2025
@dependabot dependabot bot deleted the dependabot/pip/Project/mlops/mlruns/377601592381563769/0f09b45dcd764f7780eff72819b39508/artifacts/Female_Model/pip-9e7f04bd76 branch June 24, 2025 04:49
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