MLflow 2.20.3 is a patch release includes several major features and improvements

Features:

• Implemented GPU metrics for AMD/HIP GPUs (#12694, @evenmn)
• Add txtai tracing integration (#14712, @B-Step62)
• Support new Google GenAI SDK (#14576, @TomeHirata)
• Support the new thinking content block in Anthropic Claude 3.7 models (#14733, @B-Step62)

Bug fixes:

• Resolve LangGraph tracing bug with astream_event API (#14598, @B-Step62)

Small bug fixes and documentation updates:

#14640, #14574, #14593, @serena-ruan; #14338, #14693, #14664, #14663, #14377, @B-Step62; #14680, @JulesLandrySimard; #14388, #14685, @harupy; #14704, @brilee; #14698, #14658, @bbqiu; #14660, #14659, #14632, #14616, #14594, @TomeHirata; #14535, @njbrake

MLflow 2.20.2 is a patch release includes several bug fixes and features

Features:

• [Tracing] Support tracing sync/async generator function with @mlflow.trace (#14459, @B-Step62)
• [Tracing] Support generating traces from DSPy built-in compilation and evaluation (#14400, @B-Step62)
• [Models] ChatAgent interface enhancements and Langgraph connectors updates (#14368, #14567, @bbqiu)
• [Models] VariantType support in spark_udf (#14317, @serena-ruan)

Bug fixes:

• [Models] DSPy thread issue fix (#14471, @chenmoneygithub)

Documentation updates:

• [Docs] ChatAgent documentation updates (#14367, @bbqiu)

Small bug fixes and documentation updates:

#14410, #14569, #14440, @harupy; #14510, #14544, #14491, #14488, @bbqiu; #14518, @serena-ruan; #14517, #14500, #14461, #14478, @TomeHirata; #14512, @shaikmoeed; #14496, #14473, #14475, @B-Step62; #14467, @seal07; #14022, #14453, #14539, @daniellok-db; #14450, @BenWilson2; #14449, @SaiMadhavanG

MLflow 2.20.1 is a patch release includes several bug fixes and features:

Features:

• Spark_udf support for the model signatures based on type hints (#14265, @serena-ruan)
• Helper connectors to use ChatAgent with LangChain and LangGraph (#14215, @bbqiu)
• Update classifier evaluator to draw RUC/Lift curves for CatBoost models by default (#14333, @singh-kristian)

Bug fixes:

• Fix Pydantic 1.x incompatibility issue (#14332, @BenWilson2)
• Apply temporary fix for LiteLLM tracing to workaround BerriAI/litellm#8013 (#14340, @B-Step62)
• Fix false alert from type hint based model signature for ChatModel (#14343, @B-Step62)

Other small updates:

#14337, #14382, @B-Step62; #14356, @daniellok-db, #14354, @artjen, #14360, @TomuHirata,

We are excited to announce the release of MLflow 2.20.0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• 💡Type Hint-Based Model Signature: Define your model's signature in the most Pythonic way. MLflow now supports defining a model signature based on the type hints in your PythonModel's predict function, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)

• 🧠 Bedrock / Groq Tracing Support: MLflow Tracing now offers a one-line auto-tracing experience for Amazon Bedrock and Groq LLMs. Track LLM invocation within your model by simply adding mlflow.bedrock.tracing or mlflow.groq.tracing call to the code. (#14018, @B-Step62, #14006, @anumita0203)

• 🗒️ Inline Trace Rendering in Jupyter Notebook: MLflow now supports rendering a trace UI within the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. Check out this blog post for a quick demo! (#13955, @daniellok-db)

• ⚡️Faster Model Validation with uv Package Manager: MLflow has adopted uv, a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the mlflow.models.predict API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)

• 🖥️ New Chat Panel in Trace UI: THe MLflow Trace UI now shows a unified chat panel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)

Other Features:

• Introduced ChatAgent base class for defining custom python agent (#13797, @bbqiu)
• Supported Tool Calling in DSPy Tracing (#14196, @B-Step62)
• Applied timeout override to within-request local scoring server for Spark UDF inference (#14202, @BenWilson2)
• Supported dictionary type for inference params (#14091, @serena-ruan)
• Make context parameter optional for calling PythonModel instance (#14059, @serena-ruan)
• Set default task for ChatModel (#14068, @stevenchen-db)

Bug fixes:

• [Tracking] Fix filename encoding issue in log_image (#14281, @TomeHirata)
• [Models] Fix the faithfulness metric for custom override parameters supplied to the callable metric implementation (#14220, @BenWilson2)
• [Artifacts] Update presigned URL list_artifacts to return an empty list instead of an exception (#14203, @arpitjasa-db)
• [Tracking] Fix rename permission model registry (#14139, @MohamedKHALILRouissi)
• [Tracking] Fix hard-dependency to langchain package in autologging (#14125, @B-Step62)
• [Tracking] Fix constraint name for MSSQL in migration 0584bdc529eb (#14146, @daniellok-db)
• [Scoring] Fix uninitialized loaded_model variable (#14109, @yang-chengg)
• [Model Registry] Return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts is called on a file (#14027, @shichengzhou-db)

Documentation updates:

• [Docs] Add a quick guide for how to host MLflow on various platforms (#14289, @B-Step62)
• [Docs] Improve documentation for 'artifact_uri' in 'download_artifacts' (#14225, @vinayakkgarg)
• [Docs] Add a page for search_traces (#14033, @TomeHirata)

Small bug fixes and documentation updates:

#14294, #14252, #14233, #14205, #14217, #14172, #14188, #14167, #14166, #14163, #14162, #14161, #13971, @TomeHirata; #14299, #14280, #14279, #14278, #14272, #14270, #14268, #14269, #14263, #14258, #14222, #14248, #14128, #14112, #14111, #14093, #14096, #14095, #14090, #14089, #14085, #14078, #14074, #14070, #14053, #14060, #14035, #14014, #14002, #14000, #13997, #13996, #13995, @harupy; #14298, #14286, #14249, #14276, #14259, #14242, #14254, #14232, #14207, #14206, #14185, #14196, #14193, #14173, #14164, #14159, #14165, #14152, #14151, #14126, #14069, #13987, @B-Step62; #14295, #14265, #14271, #14262, #14235, #14239, #14234, #14228, #14227, #14229, #14218, #14216, #14213, #14208, #14204, #14198, #14187, #14181, #14177, #14176, #14156, #14169, #14099, #14086, #13983, @serena-ruan; #14155, #14067, #14140, #14132, #14072, @daniellok-db; #14178, @emmanuel-ferdman; #14247, @dbczumar; #13789, #14108, @dsuhinin; #14212, @aravind-segu; #14223, #14191, #14084, @dsmilkov; #13804, @kriscon-db; #14158, @Lodewic; #14148, #14147, #14115, #14079, #14116, @WeichenXu123; #14135, @brilee; #14133, @manos02; #14121, @LeahKorol; #14025, @nojaf; #13948, @benglewis; #13942, @justsomerandomdude264; #14003, @Ajay-Satish-01; #13982, @prithvikannan; #13638, @MaxwellSalmon

Release Candidate

MLflow 2.20.0rc0 is a release candidate for 2.20.0. To install, run the following command:

pip install mlflow==2.20.0rc0

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

Major New Features

• 💡Type Hint-Based Model Signature: Define your model's signature in the most Pythonic way. MLflow now supports defining a model signature based on the type hints in your PythonModel's predict function, and validating input data payloads against it. (#14182, #14168, #14130, #14100, #14099, @serena-ruan)

• 🧠 Bedrock / Groq Tracing Support: MLflow Tracing now offers a one-line auto-tracing experience for Amazon Bedrock and Groq LLMs. Track LLM invocation within your model by simply adding mlflow.bedrock.tracing or mlflow.groq.tracing call to the code. (#14018, @B-Step62, #14006, @anumita0203)

• 🗒️ Inline Trace Rendering in Jupyter Notebook: MLflow now supports rendering a trace UI within the notebook where you are running models. This eliminates the need to frequently switch between the notebook and browser, creating a seamless local model debugging experience. (#13955, @daniellok-db)

• ⚡️Faster Model Validation with uv Package Manager: MLflow has adopted uv, a new Rust-based, super-fast Python package manager. This release adds support for the new package manager in the mlflow.models.predict API, enabling faster model environment validation. Stay tuned for more updates! (#13824, @serena-ruan)

• 🖥️ New Chat Panel in Trace UI: THe MLflow Trace UI now shows a unified chat panel for LLM invocations. The update allows you to view chat messages and function calls in a rich and consistent UI across LLM providers, as well as inspect the raw input and output payloads. (#14211, @TomuHirata)

Other Features:

• Introduced ChatAgent base class for defining custom python agent (#13797, @bbqiu)
• Supported Tool Calling in DSPy Tracing (#14196, @B-Step62)
• Added support for invokers rights in Databricks Resources (#14212, @aravind-segu)
• Applied timeout override to within-request local scoring server for Spark UDF inference (#14202, @BenWilson2)
• Supported dictionary type for inference params (#14091, @serena-ruan)
• Make context parameter optional for calling PythonModel instance (#14059, @serena-ruan)
• Set default task for ChatModel (#14068, @stevenchen-db)

We are excited to announce the release of MLflow 2.19.0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• ChatModel enhancements - ChatModel now adopts ChatCompletionRequest and ChatCompletionResponse as its new schema. The predict_stream interface uses ChatCompletionChunk to deliver true streaming responses. Additionally, the custom_inputs and custom_outputs fields in ChatModel now utilize AnyType, enabling support for a wider variety of data types. Note: In a future version of MLflow, ChatParams (and by extension, ChatCompletionRequest) will have the default values for n, temperature, and stream removed. (#13782, #13857, @stevenchen-db)

• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @B-Step62)

• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @TomeHirata, #13851, @gabrielfu)

• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @serena-ruan)

Other Features:

• [Tracking] Add update_current_trace API for adding tags to an active trace. (#13828, @B-Step62)
• [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @djliden)
• [Models] Support uv in mlflow.models.predict (#13824, @serena-ruan)
• [Models] Add type hints support including pydantic models (#13924, @serena-ruan)
• [Tracking] Add the trace.search_spans() method for searching spans within traces (#13984, @B-Step62)

Bug fixes:

• [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @WeichenXu123)
• [Tracking] Fix mlflow.end_run inside a MLflow run context manager (#13888, @WeichenXu123)
• [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @WeichenXu123)
• [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @B-Step62)
• [Models] Support saving all langchain runnables using code-based logging (#13821, @serena-ruan)
• [Model Registry] return empty array when DatabricksSDKModelsArtifactRepository.list_artifacts is called on a file (#14027, @shichengzhou-db)
• [Tracking] Stringify param values in client.log_batch() (#14015, @B-Step62)
• [Tracking] Remove deprecated squared parameter (#14028, @B-Step62)
• [Tracking] Fix request/response field in the search_traces output (#13985, @B-Step62)

Documentation updates:

• [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @B-Step62)

Small bug fixes and documentation updates:

#13972, #13968, #13917, #13912, #13906, #13846, @serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @B-Step62; #13913, #13848, #13774, @TomeHirata; #13936, #13954, #13883, @daniellok-db; #13947, @AHB102; #13929, #13922, @Ajay-Satish-01; #13857, @stevenchen-db; #13773, @BenWilson2; #13705, @williamjamir; #13745, #13743, @WeichenXu123; #13895, @chenmoneygithub; #14023, @theBeginner86

We are excited to announce the release of MLflow 2.19.0rc0! This release includes a number of significant features, enhancements, and bug fixes.

Major New Features

• ChatModel enhancements - ChatModel now adopts ChatCompletionRequest and ChatCompletionResponse as its new schema. The predict_stream interface uses ChatCompletionChunk to deliver true streaming responses. Additionally, the custom_inputs and custom_outputs fields in ChatModel now utilize AnyType, enabling support for a wider variety of data types. (#13782, #13857, @stevenchen-db)
• Any Type in model signature - MLflow now supports AnyType in model signature. It can be used to host any data types that were not supported before. (#13766, @serena-ruan)
• Tracing improvements - MLflow Tracing now supports both automatic and manual tracing for DSPy, LlamaIndex and Langchain flavors. Tracing feature is also auto-enabled for mlflow evaluation for all supported flavors. (#13790, #13793, #13795, #13897, @B-Step62)
• New Tracing Integrations - MLflow Tracing now supports CrewAI and Anthropic, enabling a one-line, fully automated tracing experience. (#13903, @TomeHirata, #13851, @gabrielfu)

Other Features:

• [Tracking] Add update_current_trace API for adding tags to an active trace. (#13828, @B-Step62)
• [Deployments] Update databricks deployments to support AI gateway & additional update endpoints (#13513, @djliden)

Bug fixes:

• [Tracking] Allow passing in spark connect dataframes in mlflow evaluate API (#13889, @WeichenXu123)
• [Tracking] Fix mlflow.end_run inside a MLflow run context manager (#13888, @WeichenXu123)
• [Scoring] Fix spark_udf conditional check on remote spark-connect client or Databricks Serverless (#13827, @WeichenXu123)
• [Models] Allow changing max_workers for built-in LLM-as-a-Judge metrics (#13858, @B-Step62)
• [Models] Support saving all langchain runnables using code-based logging (#13821, @serena-ruan)

Documentation updates:

• [Docs] Add Ollama and Instructor examples in tracing doc (#13937, @B-Step62)

Small bug fixes and documentation updates:

#13972, #13968, #13917, #13912, #13906, #13846, @serena-ruan; #13969, #13959, #13957, #13958, #13925, #13882, #13879, #13881, #13869, #13870, #13868, #13854, #13849, #13847, #13836, #13823, #13811, #13820, #13775, #13768, #13764, @harupy; #13960, #13914, #13862, #13892, #13916, #13918, #13915, #13878, #13891, #13863, #13859, #13850, #13844, #13835, #13818, #13762, @B-Step62; #13913, #13848, #13774, @TomeHirata; #13936, #13954, #13883, @daniellok-db; #13947, @AHB102; #13929, #13922, @Ajay-Satish-01; #13773, @BenWilson2; #13705, @williamjamir; #13745, #13743, @WeichenXu123; #13895, @chenmoneygithub

We are excited to announce the release of MLflow 2.18.0! This release includes a number of significant features, enhancements, and bug fixes.

Python Version Update

Python 3.8 is now at an end-of-life point. With official support being dropped for this legacy version, MLflow now requires Python 3.9
as a minimum supported version.

Note: If you are currently using MLflow's ChatModel interface for authoring custom GenAI applications, please ensure that you
have read the future breaking changes section below.

Major New Features

• 🦺 Fluent API Thread/Process Safety - MLflow's fluent APIs for tracking and the model registry have been overhauled to add support for both thread and multi-process safety. You are now no longer forced to use the Client APIs for managing experiments, runs, and logging from within multiprocessing and threaded applications. (#13456, #13419, @WeichenXu123)

• 🧩 DSPy flavor - MLflow now supports logging, loading, and tracing of DSPy models, broadening the support for advanced GenAI authoring within MLflow. Check out the MLflow DSPy Flavor documentation to get started! (#13131, #13279, #13369, #13345, @chenmoneygithub, #13543, #13800, #13807, @B-Step62, #13289, @michael-berk)

• 🖥️ Enhanced Trace UI - MLflow Tracing's UI has undergone a significant overhaul to bring usability and quality of life updates to the experience of auditing and investigating the contents of GenAI traces, from enhanced span content rendering using markdown to a standardized span component structure, (#13685, #13357, #13242, @daniellok-db)

• 🚄 New Tracing Integrations - MLflow Tracing now supports DSPy, LiteLLM, and Google Gemini, enabling a one-line, fully automated tracing experience. These integrations unlock enhanced observability across a broader range of industry tools. Stay tuned for upcoming integrations and updates! (#13801, @TomeHirata, #13585, @B-Step62)

• 📊 Expanded LLM-as-a-Judge Support - MLflow now enhances its evaluation capabilities with support for additional providers, including Anthropic, Bedrock, Mistral, and TogetherAI, alongside existing providers like OpenAI. Users can now also configure proxy endpoints or self-hosted LLMs that follow the provider API specs by using the new proxy_url and extra_headers options. Visit the LLM-as-a-Judge documentation for more details! (#13715, #13717, @B-Step62)

• ⏰ Environment Variable Detection - As a helpful reminder for when you are deploying models, MLflow now detects and reminds users of environment variables set during model logging, ensuring they are configured for deployment. In addition to this, the mlflow.models.predict utility has also been updated to include these variables in serving simulations, improving pre-deployment validation. (#13584, @serena-ruan)

Breaking Changes to ChatModel Interface

• ChatModel Interface Updates - As part of a broader unification effort within MLflow and services that rely on or deeply integrate
  with MLflow's GenAI features, we are working on a phased approach to making a consistent and standard interface for custom GenAI
  application development and usage. In the first phase (planned for release in the next few releases of MLflow), we are marking
  several interfaces as deprecated, as they will be changing. These changes will be:

  • Renaming of Interfaces:
    • ChatRequest → ChatCompletionRequest to provide disambiguation for future planned request interfaces.
    • ChatResponse → ChatCompletionResponse for the same reason as the input interface.
    • metadata fields within ChatRequest and ChatResponse → custom_inputs and custom_outputs, respectively.
  • Streaming Updates:
    • predict_stream will be updated to enable true streaming for custom GenAI applications. Currently, it returns a generator with synchronous outputs from predict. In a future release, it will return a generator of ChatCompletionChunks, enabling asynchronous streaming. While the API call structure will remain the same, the returned data payload will change significantly, aligning with LangChain’s implementation.
  • Legacy Dataclass Deprecation:
    • Dataclasses in mlflow.models.rag_signatures will be deprecated, merging into unified ChatCompletionRequest, ChatCompletionResponse, and ChatCompletionChunks.

Other Features:

• [Evaluate] Add Huggingface BLEU metrics to MLflow Evaluate (#12799, @nebrass)
• [Models / Databricks] Add support for spark_udf when running on Databricks Serverless runtime, Databricks connect, and prebuilt python environments (#13276, #13496, @WeichenXu123)
• [Scoring] Add a model_config parameter for pyfunc.spark_udf for customization of batch inference payload submission (#13517, @WeichenXu123)
• [Tracing] Standardize retriever span outputs to a list of MLflow Documents (#13242, @daniellok-db)
• [UI] Add support for visualizing and comparing nested parameters within the MLflow UI (#13012, @jescalada)
• [UI] Add support for comparing logged artifacts within the Compare Run page in the MLflow UI (#13145, @jescalada)
• [Databricks] Add support for resources definitions for Langchain model logging (#13315, @sunishsheth2009)
• [Databricks] Add support for defining multiple retrievers within dependencies for Agent definitions (#13246, @sunishsheth2009)

Bug fixes:

• [Database] Cascade deletes to datasets when deleting experiments to fix a bug in MLflow's gc command when deleting experiments with logged datasets (#13741, @daniellok-db)
• [Models] Fix a bug with Langchain's pyfunc predict input conversion (#13652, @serena-ruan)
• [Models] Fix signature inference for subclasses and Optional dataclasses that define a model's signature (#13440, @bbqiu)
• [Tracking] Fix an issue with async logging batch splitting validation rules (#13722, @WeichenXu123)
• [Tracking] Fix an issue with LangChain's autologging thread-safety behavior (#13672, @B-Step62)
• [Tracking] Disable support for running spark autologging in a threadpool due to limitations in Spark (#13599, @WeichenXu123)
• [Tracking] Mark role and index as required for chat schema (#13279, @chenmoneygithub)
• [Tracing] Handle raw response in openai autolog (#13802, @harupy)
• [Tracing] Fix a bug with tracing source run behavior when running inference with multithreading on Langchain models (#13610, @WeichenXu123)

Documentation updates:

• [Docs] Add docstring warnings for upcoming changes to ChatModel (#13730, @stevenchen-db)
• [Docs] Add a contributor's guide for implementing tracing integrations (#13333, @B-Step62)
• [Docs] Add guidance in the use of model_config when logging models as code (#13631, @sunishsheth2009)
• [Docs] Add documentation for the use of custom library artifacts with the code_paths model logging feature (#13702, @TomeHirata)
• [Docs] Improve SparkML log_model documentation with guidance on how return probabilities from classification models (#13684, @WeichenXu123)

Small bug fixes and documentation updates:

#13775, #13768, #13764, #13744, #13699, #13742, #13703, #13669, #13682, #13569, #13563, #13562, #13539, #13537, #13533, #13408, #13295, @serena-ruan; #13768, #13764, #13761, #13738, #13737, #13735, #13734, #13723, #13726, #13662, #13692, #13689, #13688, #13680, #13674, #13666, #13661, #13625, #13460, #13626, #13546, #13621, #13623, #13603, #13617, #13614, #13606, #13600, #13583, #13601, #13602, #13604, #13598, #13596, #13597, #13531, #13594, #13589, #13581, #13112, #13587, #13582, #13579, #13578, #13545, #13572, #13571, #13564, #13559, #13565, #13558, #13541, #13560, #13556, #13534, #13386, #13532, #13385, #13384, #13383, #13507, #13523, #13518, #13492, #13493, #13487, #13490, #13488, #13449, #13471, #13417, #13445, #13430, #13448, #13443, #13429, #13418, #13412, #13382, #13402, #13381, #13364, #13356, #13309, #13313, #13334, #13331, #13273, #13322, #13319, #13308, #13302, #13268, #13298, #13296, @harupy; #13705, @williamjamir; #13632, @shichengzhou-db; #13755, #13712, #13260, @BenWilson2; #13745, #13743, #13697, #13548, #13549, #13577, #13349, #13351, #13350, #13342, #13341, @WeichenXu123; #13807, #13798, #13787, #13786, #13762, #13749, #13733, #13678, #13721, #13611, #13528, #13444, #13450, #13360, #13416, #13415, #13336, #13305, #13271, @B-Step62; #13808, #13708, @smurching; #13739, @fedorkobak; #13728, #13719, #13695, #13677, @TomeHirata; #13776, #13736, #13649, #13285, #13292, #13282, #13283, #13267, @daniellok-db; #13711, @bhavya2109sharma; #13693, #13658, @aravind-segu; #13553, @dsuhinin; #13663, @gitlijian; #13657, #13629, @parag-shendye; #13630, @JohannesJungbluth; #13613, @itepifanio; #13480, @agjendem; #13627, @ilyaresh; #13592, #13410, #13358, #13233, @nojaf; #13660, #13505, @sunishsheth2009; #13414, @lmoros-DB; #13399, @Abubakar17; #13390, @KekmaTime; #13291, @michael-berk; #12511, @jgiannuzzi; #13265, @Ahar28; #13785, @Rick-McCoy; #13676, @hyolim-e; #13718, @annzhang-db; #13705, @williamjamir

We are excited to announce the release candidate for MLflow 2.18.0!
The 2.18.0 release includes a number of signficant features, enhancements, and bug fixes.

Python Version Update

Python 3.8 is now at an end-of-life point. With official support being dropped for this legacy version, MLflow now requires Python 3.9 as a minimum supported version (@harupy)

Note: If you are currently using MLflow's ChatModel interface for authoring custom GenAI applications, please ensure that you
have read the future breaking changes section below.

Breaking Changes to Experimental Features

• ChatModel Interface Changes - As part of a broader unification effort within MLflow and services that rely on or deeply integrate
  with MLflow's GenAI features, we are working on a phased approach to making a consistent and standard interface for custom GenAI
  application development and usage. In the first phase (planned for release in the next few releases of MLflow), we are marking
  several interfaces as deprecated, as they will be changing. These changes will be:

  • Renaming of ChatModel Interfaces
    • ChatRequest is being renamed to ChatCompletionRequest to provide disambiguation for future planned request interface
      types. ChatRequest is too generic for planned future work.
    • ChatResponse is being renamed to ChatCompletionResponse for the same reason as the input interface.
    • predict_stream is being updated to provide actual streaming capabilities for custom GenAI applications. Currently, the return type of
      predict_stream is a generator containing the synchronous output from a call to predict. In a future release, this will be changing to
      return a generator of Chunks. While your existing call structure for the predict_stream API won't change, the returned data payload will
      change significantly and allow for a true streaming return as asynchronous streaming values are returned. The updated return type will be
      a generator of ChatCompletionChunks, similar to the existing implementation for LangChain.
    • The mutable components of ChatRequest and ChatResponse, both currently set as metadata fields, will be renamed to the more specific
      respective custom_inputs and custom_outputs. These field names will be made consistent with future GenAI interfaces as well.
  • Deprecation of Rag Signatures
    • In an effort to reduce the complexity with interfaces to different systems, we will be marking the dataclasses defined within
      mlflow.models.rag_signatures as deprecated in a future release and merging these with the unified signature definitions and data
      structures within ChatCompletionRequest, ChatCompletionResponse and ChatCompletionChunks.

Major New Features

• Fluent API Thread / Process Safety - MLflow's fluent APIs for tracking and the model registry have been overhauled to add support for both thread and multi-process safety.
  You are now no longer forced to use the Client APIs for managing experiments, runs, and logging from within multiprocessing and threaded applications. (#13456, #13419, @WeichenXu123)

• Broad Support for LLM-as-a-judge endpoints - Prior to this release, MLflow's evaluate functionality for metrics that use an LLM to generate
  metric scores was restricted to a restrictive list of providers (defaulted to use either OpenAI public APIs, Databricks endpoints, or AzureOpenAI
  endpoints. (#13715, #13717, @B-Step62)

  This restriction has been corrected to support:

  • OpenAI-compatible endpoints - whether you're running a proxy to OpenAI or are creating a self-hosted LLM that conforms to the OpenAI specification
    standards, you will now be able to define a proxy_url and specify extra_headers to pass along with your evaluation requests to use MLflow evaluate
    to interface to whatever LLM you would like to use as a judge.
  • Additional Providers - We now support using Anthropic, Bedrock, Mistral, and TogetherAI in addition to OpenAI for viable LLM interfaces for
    judges. Custom proxy urls and headers are supported for these additional provider interfaces as well.

• Enhanced Trace UI - From enhanced span content rendering using markdown to a standardized span component structure, MLflow's trace UI has undergone
  a significant overhaul to bring usability and quality of life updates to the experience of auditing and investigating the contents of GenAI traces. (#13685, #13357, #13242, @daniellok-db)

• DSPy flavor - MLflow now supports logging, loading, and tracing of DSPy models, broadening the support for advanced GenAI authoring within MLflow. (#13131, #13279, #13369, #13345, @chenmoneygithub), (#13543, @B-Step62)

• Detection of Environment Variable dependencies - As a helpful reminder for when you are deploying models, MLflow will now record detected environment variables that are set
  within your model logging environment and provider reminders to set these values when deploying. In addition to this, updates have been made to the pre-deployment validation
  utility mlflow.models.predict to include required environment variables to the subprocess serving simulation to ensure that you can validate your model's deployment compatibility
  prior to deployment. (#13584, @serena-ruan)

Features:

• [Evaluate] Add expanded support for additional LLM providers and custom endpoints for GenAI judge metrics. (#13715, #13717, @B-Step62)
• [Evaluate] Add Huggingface BLEU metrics to MLflow Evaluate (#12799, @nebrass)
• [Models] Add dspy flavor to MLflow (#13131, #13279, #13369, #13345, @chenmoneygithub)
• [Models] Add tracing support for DSPy models (#13543, @B-Step62)
• [Models] Add environment variable detection when logging models (#13584, @serena-ruan)
• [Models] Add support for the new LlamaIndex Workflow API when logging (#13277, @B-Step62)
• [Models / Databricks] Add support for spark_udf when running on Databricks Serverless runtime, Databricks connect, and prebuilt python environments (#13276, #13496, @WeichenXu123)
• [Scoring] Add a model_config parameter for pyfunc.spark_udf for customization of batch inference payload submission (#13517, @WeichenXu123)
• [Tracing] Standardize retriever span outputs to a list of MLflow Documents (#13242, @daniellok-db)
• [Tracing] Add support for tracing OpenAI Swarm models (#13497, @B-Step62)
• [Tracking] Make MLflow fluent APIs thread and process safe (#13456, #13419, @WeichenXu123)
• [Tracking / Databricks] Add support for resources definitions for Langchain model logging (#13315, @sunishsheth2009)
• [Tracking / Databricks] Add support for defining multiple retrievers within dependencies for Agent definitions (#13246, @sunishsheth2009)
• [UI] Add significant updates to MLflow's tracing UI for enhanced content rendering and span structure display (#13685, #13357 @daniellok-db)
• [UI] Add support for visualizing and comparing nested parameters within the MLflow UI (#13012, @jescalada)
• [UI] Add support for comparing logged artifacts within the Compare Run page in the MLflow UI (#13145, @jescalada)

Bug fixes:

• [Database] Cascade deletes to datasets when deleting experiments to fix a bug in MLflow's gc command when deleting experiments with logged datasets (#13741, @daniellok-db)
• [Models] Fix a bug with Langchain's pyfunc predict input conversion (#13652, @serena-ruan)
• [Models] Update Databricks dependency extraction to handle the partner package. (#13266, @B-Step62)
• [Models] Fix signature inference for subclasses and Optional dataclasses that define a model's signature (#13440, @bbqiu)
• [Tracking] Fix an issue with async logging batch splitting validation rules (#13722, @WeichenXu123)
• [Tracking] Fix an issue with LangChain's autologging thread-safety behavior (#13672, @B-Step62)
• [Tracking] Fix a bug with tracing source run behavior when running inference with multithreading on Langchain models (#13610, @WeichenXu123)
• [Tracking] Disable support for running spark autologging in a threadpool due to limitations in Spark (#13599, @WeichenXu123)
• [Tracking] Mark role and index as required for chat schema (#13279, @chenmoneygithub)

Documentation updates:

• [Docs] Add docstring warnings for upcoming changes to ChatModel (#13730, @stevenchen-db)
• [Docs] Add documentation for the use of custom library artifacts with the code_paths model logging feature (#13702, @TomeHirata)
• [Docs] Improve SparkML log_model documentation with guidance on how return probabilities from classification models (#13684, @WeichenXu123)
• [Docs] Add guidance in the use of model_config when logging models as code (#13631, @sunishsheth2009)
• [Docs] Add documentation for the DSPy flavor (#13289, @michael-berk)
• [Docs] Add a contributor's guide for implementing tracing integrations (#13333, @B-Step62)
• [Docs] Add run_id parameter to the search_trace API (#13251, @B-Step62)

Small bug fixes and documentation updates:

#13744, #13699, #13742, #13703, #13669, #13682, #13569, #13563, #13562, #13539, #13537, #13533, #13408, #13295, @serena-ruan; #13768, #13764, #13761, #13738, #13737, #13735, #13734, #13723, #13726, #13662, #13692, #13689, #13688, #13680, #13674, #13666, #13661, #13625, #13460, #13626, #13546, #13621, #13623, #13603, #13617, #13614, #13606, #13600, #13583, #13601, #13602, #13604, #13598, #13596, #13597, #13531, #13594, #13589, #13581, #13112, #13587, #13582, #13579, #13578, #13545, #13572, #13571, #13564, #13559, #13565, #13558, #13541, #13560, #13556, #13534, #13386, #13532, #13385, #13384, #13383, #13507, #13523, #13518, #13492, #13493, #13487, #13490, #13488, #13449, #13471, #13417, #13445, #13430, #13448, #13443, #13429, #13418, #13412, #13382, #13402, #13381, #13364, #13356, #13309, #13313, #13334, #13331, ...

MLflow 2.17.2 includes several major features and improvements

Features:

• [Model Registry] DatabricksSDKModelsArtifactRepository support (#13203, @shichengzhou-db)
• [Tracking] Support extracting new UCFunctionToolkit as model resources (#13567, @serena-ruan)

Bug fixes:

• [Models] Fix RunnableBinding saving (#13566, @B-Step62)
• [Models] Pin numpy when pandas < 2.1.2 in pip requirements (#13580, @serena-ruan)

Documentation updates:

• [Docs] ChatModel tool calling tutorial (#13542, @daniellok-db)

Small bug fixes and documentation updates:

#13569, @serena-ruan; #13595, @BenWilson2; #13593, @mnijhuis-dnb;