MLflow 2.10.1

MLflow 2.10.1 is a patch release, containing fixes for various bugs in the transformers and langchain flavors, the MLflow UI, and the S3 artifact store. More details can be found in the patch notes below.

Bug fixes:

• [UI] Fixed a bug that prevented datasets from showing up in the MLflow UI (#10992, @daniellok-db)
• [Artifact Store] Fixed directory bucket region name retrieval (#10967, @kriscon-db)
• Bug fixes for Transformers flavor
  • [Models] Fix an issue with transformer pipelines not inheriting the torch dtype specified on the model, causing pipeline inference to consume more resources than expected. (#10979, @B-Step62)
  • [Models] Fix non-idempotent prediction due to in-place update to model-config (#11014, @B-Step62)
  • [Models] Fixed a bug affecting prompt templating with Text2TextGeneration pipelines. Previously, calling predict() on a pyfunc-loaded Text2TextGeneration pipeline would fail for string and List[string] inputs. (#10960, @B-Step62)
• Bug fixes for Langchain flavor
  • Fixed errors that occur when logging inputs and outputs with different lengths (#10952, @serena-ruan)

Documentation updates:

• [Docs] Add indications of DL UI capabilities to the DL landing page (#10991, @BenWilson2)
• [Docs] Fix incorrect logo on LLMs landing page (#11017, @BenWilson2)

Small bug fixes and documentation updates:

#10930, #11005, @serena-ruan; #10927, @harupy

MLflow 2.10.0

In MLflow 2.10, we're introducing a number of significant new features that are preparing the way for current and future enhanced support for Deep Learning use cases, new features to support a broadened support for GenAI applications, and some quality of life improvements for the MLflow Deployments Server (formerly the AI Gateway).

New MLflow Website

We have a new home. The new site landing page is fresh, modern, and contains more content than ever. We're adding new content and blogs all of the time.

Model Signature Supports Objects and Arrays (#9936, @serena-ruan)

Objects and Arrays are now available as configurable input and output schema elements. These new types are particularly useful for GenAI-focused flavors that can have complex input and output types. See the new Signature and Input Example documentation to learn more about how to use these new signature types.

Langchain Autologging (#10801, @serena-ruan)

LangChain has autologging support now! When you invoke a chain, with autologging enabled, we will automatically log most chain implementations, recording and storing your configured LLM application for you. See the new Langchain documentation to learn more about how to use this feature.

Prompt Templating for Transformers Models (#10791, @daniellok-db)

The MLflow transformers flavor now supports prompt templates. You can now specify an application-specific set of instructions to submit to your GenAI pipeline in order to simplify, streamline, and integrate sets of system prompts to be supplied with each input request. Check out the updated guide to transformers to learn more and see examples!

MLflow Deployments Server Enhancement (#10765, @gabrielfu; #10779, @TomeHirata)

The MLflow Deployments Server now supports two new requested features: (1) OpenAI endpoints that support streaming responses. You can now configure an endpoint to return realtime responses for Chat and Completions instead of waiting for the entire text contents to be completed. (2) Rate limits can now be set per endpoint in order to help control cost overrun when using SaaS models.

Further Document Improvements

Continued the push for enhanced documentation, guides, tutorials, and examples by expanding on core MLflow functionality (Deployments, Signatures, and Model Dependency management), as well as entirely new pages for GenAI flavors. Check them out today!

Other Features:

• [Models] Enhance the MLflow Models predict API to serve as a pre-logging validator of environment compatibility. (#10759, @B-Step62)
• [Models] Add support for Image Classification pipelines within the transformers flavor (#10538, @KonakanchiSwathi)
• [Models] Add support for retrieving and storing license files for transformers models (#10871, @BenWilson2)
• [Models] Add support for model serialization in the Visual NLP format for JohnSnowLabs flavor (#10603, @C-K-Loan)
• [Models] Automatically convert OpenAI input messages to LangChain chat messages for pyfunc predict (#10758, @dbczumar)
• [Tracking] Enhance async logging functionality by ensuring flush is called on Futures objects (#10715, @chenmoneygithub)
• [Tracking] Add support for a non-interactive mode for the login() API (#10623, @henxing)
• [Scoring] Allow MLflow model serving to support direct dict inputs with the messages key (#10742, @daniellok-db, @B-Step62)
• [Deployments] Add streaming support to the MLflow Deployments Server for OpenAI streaming return compatible routes (#10765, @gabrielfu)
• [Deployments] Add support for directly interfacing with OpenAI via the MLflow Deployments server (#10473, @prithvikannan)
• [UI] Introduce a number of new features for the MLflow UI (#10864, @daniellok-db)
• [Server-infra] Add an environment variable that can disallow HTTP redirects (#10655, @daniellok-db)
• [Artifacts] Add support for Multipart Upload for Azure Blob Storage (#10531, @gabrielfu)

Bug fixes

• [Models] Add deduplication logic for pip requirements and extras handling for MLflow models (#10778, @BenWilson2)
• [Models] Add support for paddle 2.6.0 release (#10757, @WeichenXu123)
• [Tracking] Fix an issue with an incorrect retry default timeout for urllib3 1.x (#10839, @BenWilson2)
• [Recipes] Fix an issue with MLflow Recipes card display format (#10893, @WeichenXu123)
• [Java] Fix an issue with metadata collection when using Streaming Sources on certain versions of Spark where Delta is the source (#10729, @daniellok-db)
• [Scoring] Fix an issue where SageMaker tags were not propagating correctly (#9310, @clarkh-ncino)
• [Windows / Databricks] Fix an issue with executing Databricks run commands from within a Window environment (#10811, @wolpl)
• [Models / Databricks] Disable mlflowdbfs mounts for JohnSnowLabs flavor due to flakiness (#9872, @C-K-Loan)

Documentation updates:

• [Docs] Fixed the KeyError: 'loss' bug for the Quickstart guideline (#10886, @yanmxa)
• [Docs] Relocate and supplement Model Signature and Input Example docs (#10838, @BenWilson2)
• [Docs] Add the HuggingFace Model Evaluation Notebook to the website (#10789, @BenWilson2)
• [Docs] Rewrite the search run documentation (#10863, @chenmoneygithub)
• [Docs] Create documentation for transformers prompt templates (#10836, @daniellok-db)
• [Docs] Refactoring of the Getting Started page (#10798, @BenWilson2)
• [Docs] Add a guide for model dependency management (#10807, @B-Step62)
• [Docs] Add tutorials and guides for LangChain (#10770, @BenWilson2)
• [Docs] Refactor portions of the Deep Learning documentation landing page (#10736, @chenmoneygithub)
• [Docs] Refactor and overhaul the Deployment documentation and add new tutorials (#10726, @B-Step62)
• [Docs] Add a PyTorch landing page, quick start, and guide (#10687, #10737 @chenmoneygithub)
• [Docs] Add additional tutorials to OpenAI flavor docs (#10700, @BenWilson2)
• [Docs] Enhance the guides on quickly getting started with MLflow by demonstrating how to use Databricks Community Edition (#10663, @BenWilson2)
• [Docs] Create the OpenAI Flavor landing page and intro notebooks (#10622, @BenWilson2)
• [Docs] Refactor the Tensorflow flavor API docs (#10662, @chenmoneygithub)

#10538, #10901, #10903, #10876, #10833, #10859, #10867, #10843, #10857, #10834, #10814, #10805, #10764, #10771, #10733, #10724, #10703, #10710, #10696, #10691, #10692, @B-Step62; #10882, #10854, #10395, #10725, #10695, #10712, #10707, #10667, #10665, #10654, #10638, #10628, @harupy; #10881, #10875, #10835, #10845, #10844, #10651, #10806, #10786, #10785, #10781, #10741, #10772, #10727, @serena-ruan; #10873, #10755, #10750, #10749, #10619, @WeichenXu123; #10877, @amueller; #10852, @QuentinAmbard; #10822, #10858, @gabrielfu; #10862, @jerrylian-db; #10840, @ernestwong-db; #10841, #10795, #10792, #10774, #10776, #10672, @BenWilson2; #10827, #10826, #10825, #10732, #10481, @michael-berk; #10828, #10680, #10629, @daniellok-db; #10799, #10800, #10578, #10782, #10783, #10723, #10464, @annzhang-db; #10803, #10731, #10708, @kriscon-db; #10797, @dbczumar; #10756, #10751, @Ankit8848; #10784, @AveshCSingh; #10769, #10763, #10717, @chenmoneygithub; #10698, @rmalani-db; #10767, @liangz1; #10682, @cdreetz; #10659, @prithvikannan; #10639, #10609, @TomeHirata

MLflow 2.9.2

MLflow 2.9.2 is a patch release, containing several critical security fixes and configuration updates to support extremely large model artifacts.

Features:

• [Deployments] Add the mlflow.deployments.openai API to simplify direct access to OpenAI services through the deployments API (#10473, @prithvikannan)
• [Server-infra] Add a new environment variable that permits disabling http redirects within the Tracking Server for enhanced security in publicly accessible tracking server deployments (#10673, @daniellok-db)
• [Artifacts] Add environment variable configurations for both Multi-part upload and Multi-part download that permits modifying the per-chunk size to support extremely large model artifacts (#10648, @harupy)

Security fixes:

• [Server-infra] Disable the ability to inject malicious code via manipulated YAML files by forcing YAML rendering to be performed in a secure Sandboxed mode (#10676, @BenWilson2, #10640, @harupy)
• [Artifacts] Prevent path traversal attacks when querying artifact URI locations by disallowing .. path traversal queries (#10653, @B-Step62)
• [Data] Prevent a mechanism for conducting a malicious file traversal attack on Windows when using tracking APIs that interface with HTTPDatasetSource (#10647, @BenWilson2)
• [Artifacts] Prevent a potential path traversal attack vector via encoded url traversal paths by decoding paths prior to evaluation (#10650, @B-Step62)
• [Artifacts] Prevent the ability to conduct path traversal attacks by enforcing the use of sanitized paths with the tracking server (#10666, @harupy)
• [Artifacts] Prevent path traversal attacks when using an FTP server as a backend store by enforcing base path declarations prior to accessing user-supplied paths (#10657, @harupy)

Documentation updates:

• [Docs] Add an end-to-end tutorial for RAG creation and evaluation (#10661, @AbeOmor)
• [Docs] Add Tensorflow landing page (#10646, @chenmoneygithub)
• [Deployments / Tracking] Add endpoints to LLM evaluation docs (#10660, @prithvikannan)
• [Examples] Add retriever evaluation tutorial for LangChain and improve the Question Generation tutorial notebook (#10419, @liangz1)

Small bug fixes and documentation updates:

#10677, #10636, @serena-ruan; #10652, #10649, #10641, @harupy; #10643, #10632, @BenWilson2

MLflow 2.9.1

MLflow 2.9.1 is a patch release, containing a critical bug fix related to loading pyfunc models that were saved in previous versions of MLflow.

Bug fixes:

• [Models] Revert Changes to PythonModel that introduced loading issues for models saved in earlier versions of MLflow (#10626, @BenWilson2)

Small bug fixes and documentation updates:

#10625, @BenWilson2

MLflow 2.9.0

MLflow 2.9.0 includes several major features and improvements.

MLflow AI Gateway deprecation (#10420, @harupy)

The feature previously known as MLflow AI Gateway has been moved to utilize the MLflow deployments API.
For guidance on migrating from the AI Gateway to the new deployments API, please see the MLflow AI Gateway Migration Guide.

MLflow Tracking docs overhaul (#10471, @B-Step62)

The MLflow tracking docs have been overhauled. We'd like your feedback on the new tracking docs!

Security fixes

Three security patches have been filed with this release and CVE's have been issued with the details involved in the security patch and potential attack vectors. Please review and update your tracking server deployments if your tracking server is not securely deployed and has open access to the internet.

• Sanitize path in HttpArtifactRepository.list_artifacts (#10585, @harupy)
• Sanitize filename in Content-Disposition header for HTTPDatasetSource (#10584, @harupy).
• Validate Content-Type header to prevent POST XSS (#10526, @B-Step62)

Features

• [Tracking] Use backoff_jitter when making HTTP requests (#10486, @ajinkyavbhandare)
• [Tracking] Add default aggregate_results if the score type is numeric in make_metric API (#10490, @sunishsheth2009)
• [Tracking] Add string type of score types for metric value for genai (#10307, @sunishsheth2009)
• [Artifacts] Support multipart upload for for proxy artifact access (#9521, @harupy)
• [Models] Support saving torch_dtype for transformers models (#10586, @serena-ruan)
• [Models] Add built-in metric ndcg_at_k to retriever evaluation (#10284, @liangz1)
• [Model Registry] Implement universal copy_model_version (#10308, @jerrylian-db)
• [Models] Support saving/loading RunnableSequence, RunnableParallel, and RunnableBranch (#10521, #10611, @serena-ruan)

Bug fixes

• [Tracking] Resume system metrics logging when resuming an existing run (#10312, @chenmoneygithub)
• [UI] Fix incorrect sorting order in line chart (#10553, @B-Step62)
• [UI] Remove extra whitespace in git URLs (#10506, @mrplants)
• [Models] Make spark_udf use NFS to broadcast model to spark executor on databricks runtime and spark connect mode (#10463, @WeichenXu123)
• [Models] Fix promptlab pyfunc models not working for chat routes (#10346, @daniellok-db)

Documentation updates

• [Docs] Add a quickstart guide for Tensorflow (#10398, @chenmoneygithub)
• [Docs] Improve the parameter tuning guide (#10344, @chenmoneygithub)
• [Docs] Add a guide for system metrics logging (#10429, @chenmoneygithub)
• [Docs] Add instructions on how to configure credentials for Azure OpenAI (#10560, @BenWilson2)
• [Docs] Add docs and tutorials for Sentence Transformers flavor (#10476, @BenWilson2)
• [Docs] Add tutorials, examples, and guides for Transformers Flavor (#10360, @BenWilson2)

Small bug fixes and documentation updates

#10567, #10559, #10348, #10342, #10264, #10265, @B-Step62; #10595, #10401, #10418, #10394, @chenmoneygithub; #10557, @dan-licht; #10584, #10462, #10445, #10434, #10432, #10412, #10411, #10408, #10407, #10403, #10361, #10340, #10339, #10310, #10276, #10268, #10260, #10224, #10214, @harupy; #10415, @jessechancy; #10579, #10555, @annzhang-db; #10540, @wllgrnt; #10556, @smurching; #10546, @mbenoit29; #10534, @gabrielfu; #10532, #10485, #10444, #10433, #10375, #10343, #10192, @serena-ruan; #10480, #10416, #10173, @jerrylian-db; #10527, #10448, #10443, #10442, #10441, #10440, #10439, #10381, @prithvikannan; #10509, @keenranger; #10508, #10494, @WeichenXu123; #10489, #10266, #10210, #10103, @TomeHirata; #10495, #10435, #10185, @daniellok-db; #10319, @michael-berk; #10417, @bbqiu; #10379, #10372, #10282, @BenWilson2; #10297, @KonakanchiSwathi; #10226, #10223, #10221, @milinddethe15; #10222, @flooxo; #10590, @letian-w;

MLflow 2.8.1

MLflow 2.8.1 is a patch release, containing some critical bug fixes and an update to our continued work on reworking our docs.

Notable details:

• The API mlflow.llm.log_predictions is being marked as deprecated, as its functionality has been incorporated into mlflow.log_table. This API will be removed in the 2.9.0 release. (#10414, @dbczumar)

Bug fixes:

• [Artifacts] Fix a regression in 2.8.0 where downloading a single file from a registered model would fail (#10362, @BenWilson2)
• [Evaluate] Fix the Azure OpenAI integration for mlflow.evaluate when using LLM judge metrics (#10291, @prithvikannan)
• [Evaluate] Change Examples to optional for the make_genai_metric API (#10353, @prithvikannan)
• [Evaluate] Remove the fastapi dependency when using mlflow.evaluate for LLM results (#10354, @prithvikannan)
• [Evaluate] Fix syntax issues and improve the formatting for generated prompt templates (#10402, @annzhang-db)
• [Gateway] Fix the Gateway configuration validator pre-check for OpenAI to perform instance type validation (#10379, @BenWilson2)
• [Tracking] Fix an intermittent issue with hanging threads when using asynchronous logging (#10374, @chenmoneygithub)
• [Tracking] Add a timeout for the mlflow.login() API to catch invalid hostname configuration input errors (#10239, @chenmoneygithub)
• [Tracking] Add a flush operation at the conclusion of logging system metrics (#10320, @chenmoneygithub)
• [Models] Correct the prompt template generation logic within the Prompt Engineering UI so that the prompts can be used in the Python API (#10341, @daniellok-db)
• [Models] Fix an issue in the SHAP model explainability functionality within mlflow.shap.log_explanation so that duplicate or conflicting dependencies are not registered when logging (#10305, @BenWilson2)

Documentation updates:

• [Docs] Add MLflow Tracking Quickstart (#10285, @BenWilson2)
• [Docs] Add tracking server configuration guide (#10241, @chenmoneygithub)
• [Docs] Refactor and improve the model deployment quickstart guide (#10322, @prithvikannan)
• [Docs] Add documentation for system metrics logging (#10261, @chenmoneygithub)

Small bug fixes and documentation updates:

#10367, #10359, #10358, #10340, #10310, #10276, #10277, #10247, #10260, #10220, #10263, #10259, #10219, @harupy; #10313, #10303, #10213, #10272, #10282, #10283, #10231, #10256, #10242, #10237, #10238, #10233, #10229, #10211, #10231, #10256, #10242, #10238, #10237, #10229, #10233, #10211, @BenWilson2; #10375, @serena-ruan; #10330, @Haxatron; #10342, #10249, #10249, @B-Step62; #10355, #10301, #10286, #10257, #10236, #10270, #10236, @prithvikannan; #10321, #10258, @jerrylian-db; #10245, @jessechancy; #10278, @daniellok-db; #10244, @gabrielfu; #10226, @milinddethe15; #10390, @bbqiu; #10232, @sunishsheth2009

MLflow 2.8.0

MLflow 2.8.0 includes several notable new features and improvements

• The MLflow Evaluate API has had extensive feature development in this release to support LLM workflows and multiple new evaluation modalities. See the new documentation, guides, and tutorials for MLflow LLM Evaluate to learn more.
• The MLflow Docs modernization effort has started. You will see a very different look and feel to the docs when visiting them, along with a batch of new tutorials and guides. More changes will be coming soon to the docs!
• 4 new LLM providers have been added! Google PaLM 2, AWS Bedrock, AI21 Labs, and HuggingFace TGI can now be configured and used within the AI Gateway. Learn more in the new AI Gateway docs!

Features:

• [Gateway] Add support for AWS Bedrock as a provider in the AI Gateway (#9598, @andrew-christianson)
• [Gateway] Add support for Huggingface Text Generation Inference as a provider in the AI Gateway (#10072, @SDonkelaarGDD)
• [Gateway] Add support for Google PaLM 2 as a provider in the AI Gateway (#9797, @arpitjasa-db)
• [Gateway] Add support for AI21labs as a provider in the AI Gateway (#9828, #10168, @zhe-db)
• [Gateway] Introduce a simplified method for setting the configuration file location for the AI Gateway via environment variable (#9822, @danilopeixoto)
• [Evaluate] Introduce default provided LLM evaluation metrics for MLflow evaluate (#9913, @prithvikannan)
• [Evaluate] Add support for evaluating inference datasets in MLflow evaluate (#9830, @liangz1)
• [Evaluate] Add support for evaluating single argument functions in MLflow evaluate (#9718, @liangz1)
• [Evaluate] Add support for Retriever LLM model type evaluation within MLflow evaluate (#10079, @liangz1)
• [Models] Add configurable parameter for external model saving in the ONNX flavor to address a regression (#10152, @daniellok-db)
• [Models] Add support for saving inference parameters in a logged model's input example (#9655, @serena-ruan)
• [Models] Add support for completions in the OpenAI flavor (#9838, @santiagxf)
• [Models] Add support for inference parameters for the OpenAI flavor (#9909, @santiagxf)
• [Models] Introduce support for configuration arguments to be specified when loading a model (#9251, @santiagxf)
• [Models] Add support for integrated Azure AD authentication for the OpenAI flavor (#9704, @santiagxf)
• [Models / Scoring] Introduce support for model training lineage in model serving (#9402, @M4nouel)
• [Model Registry] Introduce the copy_model_version client API for copying model versions across registered models (#9946, #10078, #10140, @jerrylian-db)
• [Tracking] Expand the limits of parameter value length from 500 to 6000 (#9709, @serena-ruan)
• [Tracking] Introduce support for Spark 3.5's SparkConnect mode within MLflow to allow logging models created using this operation mode of Spark (#9534, @WeichenXu123)
• [Tracking] Add support for logging system metrics to the MLflow fluent API (#9557, #9712, #9714, @chenmoneygithub)
• [Tracking] Add callbacks within MLflow for Keras and Tensorflow (#9454, #9637, #9579, @chenmoneygithub)
• [Tracking] Introduce a fluent login API for Databricks within Mlflow (#9665, #10180, @chenmoneygithub)
• [Tracking] Add support for customizing auth for http requests from the MLflow client via a plugin extension (#10049, @lu-ohai)
• [Tracking] Introduce experimental asynchronous logging support for metrics, params, and tags (#9705, @sagarsumant)
• [Auth] Modify the behavior of user creation in MLflow Authentication so that only admins can create new users (#9700, @gabrielfu)
• [Artifacts] Add support for using xethub as an artifact store via a plugin extension (#9957, @Kelton8Z)

Bug fixes:

• [Evaluate] Fix a bug with Azure OpenAI configuration usage within MLflow evaluate (#9982, @sunishsheth2009)
• [Models] Fix a data consistency issue when saving models that have been loaded in heterogeneous memory configuration within the transformers flavor (#10087, @BenWilson2)
• [Models] Fix an issue in the transformers flavor for complex input types by adding dynamic dataframe typing (#9044, @wamartin-aml)
• [Models] Fix an issue in the langchain flavor to provide support for chains with multiple outputs (#9497, @bbqiu)
• [Docker] Fix an issue with Docker image generation by changing the default env-manager to virtualenv (#9938, @Beramos)
• [Auth] Fix an issue with complex passwords in MLflow Auth to support a richer character set range (#9760, @dotdothu)
• [R] Fix a bug with configuration access when running MLflow R in Databricks (#10117, @zacdav-db)

Documentation updates:

• [Docs] Introduce the first phase of a larger documentation overhaul (#10197, @BenWilson2)
• [Docs] Add guide for LLM eval (#10058, #10199, @chenmoneygithub)
• [Docs] Add instructions on how to force single file serialization within the onnx flavor's save and log functions (#10178, @BenWilson2)
• [Docs] Add documentation for the relevance metric for MLflow evaluate (#10170, @sunishsheth2009)
• [Docs] Add a style guide for the contributing guide for how to structure pydoc strings (#9907, @mberk06)
• [Docs] Fix issues with the pytorch lightning autolog code example (#9964, @chenmoneygithub)
• [Docs] Update the example for mlflow.data.from_numpy() (#9885, @chenmoneygithub)
• [Docs] Add clear instructions for installing MLflow within R (#9835, @darshan8850)
• [Docs] Update model registry documentation to add content regarding support for model aliases (#9721, @jerrylian-db)

Small bug fixes and documentation updates:

#10202, #10189, #10188, #10159, #10175, #10165, #10154, #10083, #10082, #10081, #10071, #10077, #10070, #10053, #10057, #10055, #10020, #9928, #9929, #9944, #9979, #9923, #9842, @annzhang-db; #10203, #10196, #10172, #10176, #10145, #10115, #10107, #10054, #10056, #10018, #9976, #9999, #9998, #9995, #9978, #9973, #9975, #9972, #9974, #9960, #9925, #9920, @prithvikannan; #10144, #10166, #10143, #10129, #10059, #10123, #9555, #9619, @bbqiu; #10187, #10191, #10181, #10179, #10151, #10148, #10126, #10119, #10099, #10100, #10097, #10089, #10096, #10091, #10085, #10068, #10065, #10064, #10060, #10023, #10030, #10028, #10022, #10007, #10006, #9988, #9961, #9963, #9954, #9953, #9937, #9932, #9931, #9910, #9901, #9852, #9851, #9848, #9847, #9841, #9844, #9825, #9820, #9806, #9802, #9800, #9799, #9790, #9787, #9791, #9788, #9785, #9786, #9784, #9754, #9768, #9770, #9753, #9697, #9749, #9747, #9748, #9751, #9750, #9729, #9745, #9735, #9728, #9725, #9716, #9694, #9681, #9666, #9643, #9641, #9621, #9607, @harupy; #10200, #10201, #10142, #10139, #10133, #10090, #10086, #9934, #9933, #9845, #9831, #9794, #9692, #9627, #9626, @chenmoneygithub; #10110, @wenfeiy-db; #10195, #9895, #9880, #9679, @BenWilson2; #10174, #10177, #10109, #9706, @jerrylian-db; #10113, #9765, @smurching; #10150, #10138, #10136, @dbczumar; #10153, #10032, #9986, #9874, #9727, #9707, @serena-ruan; #10155, @shaotong-db; #10160, #10131, #10048, #10024, #10017, #10016, #10002, #9966, #9924, @sunishsheth2009; #10121, #10116, #10114, #10102, #10098, @B-Step62; #10095, #10026, #9991, @daniellok-db; #10050, @Dennis40816; #10062, #9868, @Gekko0114; #10033, @Anushka-Bhowmick; #9983, #10004, #9958, #9926, #9690, @liangz1; #9997, #9940, #9922, #9919, #9890, #9888, #9889, #9810, @TomeHirata; #9994, #9970, #9950, @lightnessofbein; #9965, #9677, @ShorthillsAI; #9906, @jessechancy; #9942, #9771, @Sai-Suraj-27; #9902, @remyleone; #9892, #9865, #9866, #9853, @montanarograziano; #9875, @Raghavan-B; #9858, @Salz0; #9878, @maksboyarin; #9882, @lukasz-gawron; #9827, @bncer; #9819, @gabrielfu; #9792, @harshk461; #9726, @Chiragasourabh; #9663, @Abhishek-TyRnT; #9670, @mberk06; #9755, @simonlsk; #9757, #9775, #9776, #9774, @AmirAflak; #9782, @garymm; #9756, @issamarabi; #9645, @shichengzhou-db; #9671, @zhe-db; #9660, @mingyu89; #9575, @akshaya-a; #9629, @pnacht; #9876, @C-K-Loan

MLflow 2.7.1

MLflow 2.7.1 is a patch release containing the following features, bug fixes and changes:

Features:

• [Gateway / Databricks] Add the set_limits and get_limits APIs for AI Gateway routes within Databricks (#9516, @zhe-db)
• [Artifacts / Databricks] Add support for parallelized download and upload of artifacts within Unity Catalog (#9498, @jerrylian-db)

Bug fixes:

• [Models / R] Fix a critical bug with the R client that prevents models from being loaded (#9624, @BenWilson2)
• [Artifacts / Databricks] Disable multi-part download functionality for UC Volumes local file destination when downloading models (#9631, @BenWilson2)

Small bug fixes and documentation updates:

#9640, @annzhang-db; #9622, @harupy

MLflow 2.7.0

MLflow 2.7.0 includes several major features and improvements

• [UI / Gateway] We are excited to announce the Prompt Engineering UI. This new addition offers a suite of tools tailored for efficient prompt development, testing, and evaluation for LLM use cases. Integrated directly into the MLflow AI Gateway, it provides a seamless experience for designing, tracking, and deploying prompt templates. To read about this new feature, see the documentation at https://mlflow.org/docs/latest/llms/prompt-engineering.html (#9503, @prithvikannan)

Features:

• [Gateway] Introduce MosaicML as a supported provider for the MLflow AI Gateway (#9459, @arpitjasa-db)
• [Models] Add support for using a snapshot download location when loading a transformers model as pyfunc (#9362, @serena-ruan)
• [Server-infra] Introduce plugin support for MLflow Tracking Server authentication (#9191, @barrywhart)
• [Artifacts / Model Registry] Add support for storing artifacts using the R2 backend (#9490, @shichengzhou-db)
• [Artifacts] Improve upload and download performance for Azure-based artifact stores (#9444, @jerrylian-db)
• [Sagemaker] Add support for deploying models to Sagemaker Serverless inference endpoints (#9085, @dogeplusplus)

Bug fixes:

• [Gateway] Fix a credential expiration bug by re-resolving AI Gateway credentials before each request (#9518, @dbczumar)
• [Gateway] Fix a bug where search_routes would raise an exception when no routes have been defined on the AI Gateway server (#9387, @QuentinAmbard)
• [Gateway] Fix compatibility issues with pydantic 2.x for AI gateway (#9339, @harupy)
• [Gateway] Fix an initialization issue in the AI Gateway that could render MLflow nonfunctional at import if dependencies were conflicting. (#9337, @BenWilson2)
• [Artifacts] Fix a correctness issue when downloading large artifacts to fuse mount paths on Databricks (#9545, @BenWilson2)

Documentation updates:

• [Docs] Add documentation for the Giskard community plugin for mlflow.evaluate (#9183, @rabah-khalek)

Small bug fixes and documentation updates:

#9605, #9603, #9602, #9595, #9597, #9587, #9590, #9588, #9586, #9584, #9583, #9582, #9581, #9580, #9577, #9546, #9566, #9569, #9562, #9564, #9561, #9528, #9506, #9503, #9492, #9491, #9485, #9445, #9430, #9429, #9427, #9426, #9424, #9421, #9419, #9409, #9408, #9407, #9394, #9389, #9395, #9393, #9390, #9370, #9356, #9359, #9357, #9345, #9340, #9328, #9329, #9326, #9304, #9325, #9323, #9322, #9319, #9314, @harupy; #9568, #9520, @dbczumar; #9593, @jerrylian-db; #9574, #9573, #9480, #9332, #9335, @BenWilson2; #9556, @shichengzhou-db; #9570, #9540, #9533, #9517, #9354, #9453, #9338, @prithvikannan; #9565, #9560, #9536, #9504, #9476, #9481, #9450, #9466, #9418, #9397, @serena-ruan; #9489, @dnerini; #9512, #9479, #9355, #9351, #9289 @chenmoneygithub; #9488, @bbqiu; #9474, @apurva-koti; #9505, @arpitjasa-db; #9261, @donour; #9336, #9414, #9353, @mberk06; #9451, @bncer; #9432, @barrywhart; #9347, @GraceBrigham; #9428, #9420, #9406, @WeichenXu123; #9410, @aloahPGF; #9396, #9384, #9372, @Godwin-T; #9373, @fabiansefranek; #9382, @Sai-Suraj-27; #9378, @saidattu2003; #9375, @Increshi; #9358, @smurching; #9366, #9330, @Dev-98; #9364, @Sandeep1005; #9349, #9348, @AmirAflak; #9308, @danilopeixoto; #9596, @ShorthillsAI; #9567, @Beramos; #9524, @rabah-khalek; #9312, @dependabot[bot]

MLflow 2.6.0

MLflow 2.6.0 includes several major features and improvements

Features:

• [Models / Scoring] Add support for passing extra params during inference for PyFunc models (#9068, @serena-ruan)
• [Gateway] Add support for MLflow serving to MLflow AI Gateway (#9199, @BenWilson2)
• [Tracking] Support save_kwargs for mlflow.log_figure to specify extra options when saving a figure (#9179, @stroblme)
• [Artifacts] Display progress bars when uploading/download artifacts (#9195, @serena-ruan)
• [Models] Add support for logging LangChain's retriever models (#8808, @liangz1)
• [Tracking] Add support to log customized tags to runs created by autologging (#9114, @thinkall)

Bug fixes:

• [Models] Fix text_pair functionality for transformers TextClassification pipelines (#9215, @BenWilson2)
• [Models] Fix LangChain compatibility with SQLDatabase (#9192, @dbczumar)
• [Tracking] Remove patching sklearn.metrics.get_scorer_names in mlflow.sklearn.autolog to avoid duplicate logging (#9095, @WeichenXu123)

Documentation updates:

• [Docs / Examples] Add examples and documentation for MLflow AI Gateway support for MLflow model serving (#9281, @BenWilson2)
• [Docs / Examples] Add sentence-transformers doc & example (#9047, @es94129)

Deprecation:

• [Models] The mlflow.mleap module has been marked as deprecated and will be removed in a future release (#9311, @BenWilson2)

Small bug fixes and documentation updates:

#9309, #9252, #9198, #9189, #9186, #9184, @BenWilson2; #9307, @AmirAflak; #9285, #9126, @dependabot[bot]; #9302, #9209, #9194, #9187, #9175, #9177, #9163, #9161, #9129, #9123, #9053, @serena-ruan; #9305, #9303, #9271, @KekmaTime; #9300, #9299, @itsajay1029; #9294, #9293, #9274, #9268, #9264, #9246, #9255, #9253, #9254, #9245, #9202, #9243, #9238, #9234, #9233, #9227, #9226, #9223, #9224, #9222, #9225, #9220, #9208, #9212, #9207, #9203, #9201, #9200, #9154, #9146, #9147, #9153, #9148, #9145, #9136, #9132, #9131, #9128, #9121, #9124, #9125, #9108, #9103, #9100, #9098, #9101, @harupy; #9292, @Aman123lug; #9290, #9164, #9157, #9086, @bncer; #9291, @kunal642; #9284, @NavneetSinghArora; #9286, #9262, #9142, @smurching; #9267, @tungbq; #9258, #9250, @Kunj125; #9167, #9139, #9120, #9118, #9097, @viktoriussuwandi; #9244, #9240, #9239, @Sai-Suraj-27; #9221, #9168, #9130, @gabrielfu; #9218, @tjni; #9216, @Rukiyav; #9158, #9051, @EdAbati; #9211, @scarlettrobe; #9049, @annzhang-db; #9140, @kriscon-db; #9141, @xAIdrian; #9135, @liangz1; #9067, @jmmonteiro; #9112, @WeichenXu123; #9106, @shaikmoeed; #9105, @Ankit8848; #9104, @arnabrahman