We are happy to announce the availability of MLflow 2.2.2!

MLflow 2.2.2 is a patch release containing the following bug fixes:

• [Model Registry] Allow source to be a local path within a run's artifact directory if a run_id is specified (#7993, @harupy)
• [Model Registry] Fix a bug where a windows UNC path is considered a local path (#7988, @WeichenXu123)
• [Model Registry] Disallow name to be a file path in FileStore.get_registered_model (#7965, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 2.2.1!

MLflow 2.2.1 is a patch release containing the following bug fixes:

• [Model Registry] Fix a bug that caused too many results to be requested by default when calling MlflowClient.search_model_versions() (#7935, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 2.2.0!

MLflow 2.2.0 includes several major features and improvements

Features:

• [Recipes] Add support for score calibration to the classification recipe (#7744, @sunishsheth2009)
• [Recipes] Add automatic label encoding to the classification recipe (#7711, @sunishsheth2009)
• [Recipes] Support custom data splitting logic in the classification and regression recipes (#7815, #7588, @sunishsheth2009)
• [Recipes] Introduce customizable MLflow Run name prefixes to the classification and regression recipes (#7746, @kamalesh0406; #7763, @sunishsheth2009)
• [UI] Add a new Chart View to the MLflow Experiment Page for model performance insights (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
• [UI] Modernize and improve parallel coordinates chart for model tuning (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
• [UI] Add typeahead suggestions to the MLflow Experiment Page search bar (#7864, @hubertzub-db, @apurva-koti, @prithvikannan, @ridhimag11, @sunishseth2009, @dbczumar)
• [UI] Improve performance of Experiments Sidebar for large numbers of experiments (#7804, @jmahlik)
• [Tracking] Introduce autologging support for native PyTorch models (#7627, @temporaer)
• [Tracking] Allow specifying model_format when autologging XGBoost models (#7781, @guyrosin)
• [Tracking] Add MLFLOW_ARTIFACT_UPLOAD_DOWNLOAD_TIMEOUT environment variable to configure artifact operation timeouts (#7783, @wamartin-aml)
• [Artifacts] Include Content-Type response headers for artifacts downloaded from mlflow server (#7827, @bali0019)
• [Model Registry] Introduce the searchModelVersions() API to the Java client (#7880, @gabrielfu)
• [Model Registry] Introduce max_results, order_by and page_token arguments to MlflowClient.search_model_versions() (#7623, @serena-ruan)
• [Models] Support logging large ONNX models by using external data (#7808, @dogeplusplus)
• [Models] Add support for logging Diviner models fit in Spark (#7800, @BenWilson2)
• [Models] Introduce MLFLOW_DEFAULT_PREDICTION_DEVICE environment variable to set the device for pyfunc model inference (#7922, @ankit-db)
• [Scoring] Publish official Docker images for the MLflow Model scoring server at github.com/mlflow/mlflow/pkgs (#7759, @dbczumar)

Bug fixes:

• [Recipes] Fix dataset format validation in the ingest step for custom dataset sources (#7638, @sunishsheth2009)
• [Recipes] Fix bug in identification of worst performing examples during training (#7658, @sunishsheth2009)
• [Recipes] Ensure consistent rendering of the recipe graph when inspect() is called (#7852, @sunishsheth2009)
• [Recipes] Correctly respect positive_class configuration in the transform step (#7626, @sunishsheth2009)
• [Recipes] Make logged metric names consistent with mlflow.evaluate() (#7613, @sunishsheth2009)
• [Recipes] Add run_id and artifact_path keys to logged MLmodel files (#7651, @sunishsheth2009)
• [UI] Fix bugs in UI validation of experiment names, model names, and tag keys (#7818, @subramaniam02)
• [Tracking] Resolve artifact locations to absolute paths when creating experiments (#7670, @bali0019)
• [Tracking] Exclude Delta checkpoints from Spark datasource autologging (#7902, @harupy)
• [Tracking] Consistently return an empty list from GetMetricHistory when a metric does not exist (#7589, @bali0019; #7659, @harupy)
• [Artifacts] Fix support for artifact operations on Windows paths in UNC format (#7750, @bali0019)
• [Artifacts] Fix bug in HDFS artifact listing (#7581, @pwnywiz)
• [Model Registry] Disallow creation of model versions with local filesystem sources in mlflow server (#7908, @harupy)
• [Model Registry] Fix handling of deleted model versions in FileStore (#7716, @harupy)
• [Model Registry] Correctly initialize Model Registry SQL tables independently of MLflow Tracking (#7704, @harupy)
• [Models] Correctly move PyTorch model outputs from GPUs to CPUs during inference with pyfunc (#7885, @ankit-db)
• [Build] Fix compatiblility issues with Python installations compiled using PYTHONOPTIMIZE=2 (#7791, @dbczumar)
• [Build] Fix compatibility issues with the upcoming pandas 2.0 release (#7899, @harupy; #7910, @dbczumar)

Documentation updates:

• [Docs] Add an example of saving and loading Spark MLlib models with MLflow (#7706, @dipanjank)
• [Docs] Add usage examples for mlflow.lightgbm APIs (#7565, @canerturkseven)
• [Docs] Add an example of custom model flavor creation with sktime (#7624, @benjaminbluhm)
• [Docs] Clarify precision_recall_auc metric calculation in mlflow.evaluate() (#7701, @BenWilson2)
• [Docs] Remove outdated example links (#7587, @asloan7)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 2.1.1!

MLflow 2.1.1 is a patch release containing the following bug fixes:

• [Scoring] Fix mlflow.pyfunc.spark_udf() type casting error on model with ColSpec input schema and make PyFuncModel.predict support dataframe with elements of numpy.ndarray type (#7592 @WeichenXu123)
• [Scoring] Make mlflow.pyfunc.scoring_server.client.ScoringServerClient support input dataframe with elements of numpy.ndarray type (#7594 @WeichenXu123)
• [Tracking] Ensure mlflow imports ML packages lazily (#7597, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 2.1.0!

MLflow 2.1.0 includes several major features and improvements

Features:

• [Recipes] Introduce support for multi-class classification (#7458, @mshtelma)
• [Recipes] Extend the pyfunc representation of classification models to output scores in addition to labels (#7474, @sunishsheth2009)
• [UI] Add user ID and lifecycle stage quick search links to the Runs page (#7462, @jaeday)
• [Tracking] Paginate the GetMetricHistory API (#7523, #7415, @BenWilson2)
• [Tracking] Add Runs search aliases for Run name and start time that correspond to UI column names (#7492, @apurva-koti)
• [Tracking] Add a /version endpoint to mlflow server for querying the server's MLflow version (#7273, @joncarter1)
• [Model Registry] Add FileStore support for the Model Registry (#6605, @serena-ruan)
• [Model Registry] Introduce an mlflow.search_registered_models() fluent API (#7428, @TSienki)
• [Model Registry / Java] Add a getRegisteredModel() method to the Java client (#6602) (#7511, @drod331)
• [Model Registry / R] Add an mlflow_set_model_version_tag() method to the R client (#7401, @leeweijie)
• [Models] Introduce a metadata field to the MLmodel specification and log_model() methods (#7237, @jdonzallaz)
• [Models] Extend Model.load() to support loading MLmodel specifications from remote locations (#7517, @dbczumar)
• [Models] Pin the major version of MLflow in Models' requirements.txt and conda.yaml files (#7364, @BenWilson2)
• [Scoring] Extend mlflow.pyfunc.spark_udf() to support StructType results (#7527, @WeichenXu123)
• [Scoring] Extend TensorFlow and Keras Models to support multi-dimensional inputs with mlflow.pyfunc.spark_udf()(#7531, #7291, @WeichenXu123)
• [Scoring] Support specifying deployment environment variables and tags when deploying models to SageMaker (#7433, @jhallard)

Bug fixes:

• [Recipes] Fix a bug that prevented use of custom early_stop functions during model tuning (#7538, @sunishsheth2009)
• [Recipes] Fix a bug in the logic used to create a Spark session during data ingestion (#7307, @WeichenXu123)
• [Tracking] Make the metric names produced by mlflow.autolog() consistent with mlflow.evaluate() (#7418, @wenfeiy-db)
• [Tracking] Fix an autologging bug that caused nested, redundant information to be logged for XGBoost and LightGBM models (#7404, @WeichenXu123)
• [Tracking] Correctly classify SQLAlchemy OperationalErrors as retryable HTTP errors (#7240, @barrywhart)
• [Artifacts] Correctly handle special characters in credentials when using FTP artifact storage (#7479, @HCTsai)
• [Models] Address an issue that prevented MLeap models from being saved on Windows (#6966, @dbczumar)
• [Scoring] Fix a permissions issue encountered when using NFS during model scoring with mlflow.pyfunc.spark_udf() (#7427, @WeichenXu123)

Documentation updates:

• [Docs] Add more examples to the Runs search documentation page (#7487, @apurva-koti)
• [Docs] Add documentation for Model flavors developed by the community (#7425, @mmerce)
• [Docs] Add an example for logging and scoring ONNX Models (#7398, @Rusteam)
• [Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_split format (#7540, @zhouyangyu)
• [Docs] Fix a typo in the model scoring REST API example for inputs with the dataframe_records format (#7361, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 2.0.1!

The 2.0.1 version of MLflow is a major milestone release that focuses on simplifying the management of end-to-end MLOps workflows, providing new feature-rich functionality, and expanding upon the production-ready MLOps capabilities offered by MLflow. Check out the MLflow 2.0 blog post for an in-depth walk through!

This release contains several important breaking changes from the 1.x API, additional major features and improvements.

Features:

• [Recipes] MLflow Pipelines is now MLflow Recipes - a framework that enables data scientists to quickly develop high-quality models and deploy them to production
• [Recipes] Add support for classification models to MLflow Recipes (#7082, @bbarnes52)
• [UI] Introduce support for pinning runs within the experiments UI (#7177, @harupy)
• [UI] Simplify the layout and provide customized displays of metrics, parameters, and tags within the experiments UI (#7177, @harupy)
• [UI] Simplify run filtering and ordering of runs within the experiments UI (#7177, @harupy)
• [Tracking] Update mlflow.pyfunc.get_model_dependencies() to download all referenced requirements files for specified models (#6733, @harupy)
• [Tracking] Add support for selecting the Keras model save_format used by mlflow.tensorflow.autolog() (#7123, @balvisio)
• [Models] Set mlflow.evaluate() status to stable as it is now a production-ready API
• [Models] Simplify APIs for specifying custom metrics and custom artifacts during model evaluation with mlflow.evaluate() (#7142, @harupy)
• [Models] Correctly infer the positive label for binary classification within mlflow.evaluate() (#7149, @dbczumar)
• [Models] Enable automated signature logging for tensorflow and keras models when mlflow.tensorflow.autolog() is enabled (#6678, @BenWilson2)
• [Models] Add support for native Keras and Tensorflow Core models within mlflow.tensorflow (#6530, @WeichenXu123)
• [Models] Add support for defining the model_format used by mlflow.xgboost.save/log_model() (#7068, @AvikantSrivastava)
• [Scoring] Overhaul the model scoring REST API to introduce format indicators for inputs and support multiple output fields (#6575, @tomasatdatabricks; #7254, @adriangonz)
• [Scoring] Add support for ragged arrays in model signatures (#7135, @trangevi)
• [Java] Add getModelVersion API to the java client (#6955, @wgottschalk)

Breaking Changes:

The following list of breaking changes are arranged by their order of significance within each category.

• [Core] Support for Python 3.7 has been dropped. MLflow now requires Python >=3.8
• [Recipes] mlflow.pipelines APIs have been replaced with mlflow.recipes
• [Tracking / Registry] Remove /preview routes for Tracking and Model Registry REST APIs (#6667, @harupy)
• [Tracking] Remove deprecated list APIs for experiments, models, and runs from Python, Java, R, and REST APIs (#6785, #6786, #6787, #6788, #6800, #6868, @dbczumar)
• [Tracking] Remove deprecated runs response field from Get Experiment REST API response (#6541, #6524 @dbczumar)
• [Tracking] Remove deprecated MlflowClient.download_artifacts API (#6537, @WeichenXu123)
• [Tracking] Change the behavior of environment variable handling for MLFLOW_EXPERIMENT_NAME such that the value is always used when creating an experiment (#6674, @BenWilson2)
• [Tracking] Update mlflow server to run in --serve-artifacts mode by default (#6502, @harupy)
• [Tracking] Update Experiment ID generation for the Filestore backend to enable threadsafe concurrency (#7070, @BenWilson2)
• [Tracking] Remove dataset_name and on_data_{name | hash} suffixes from mlflow.evaluate() metric keys (#7042, @harupy)
• [Models / Scoring / Projects] Change default environment manager to virtualenv instead of conda for model inference and project execution (#6459, #6489 @harupy)
• [Models] Move Keras model logging APIs to the mlflow.tensorflow flavor and drop support for TensorFlow Estimators (#6530, @WeichenXu123)
• [Models] Remove deprecated mlflow.sklearn.eval_and_log_metrics() API in favor of mlflow.evaluate() API (#6520, @dbczumar)
• [Models] Require mlflow.evaluate() model inputs to be specified as URIs (#6670, @harupy)
• [Models] Drop support for returning custom metrics and artifacts from the same function when using mlflow.evaluate(), in favor of custom_artifacts (#7142, @harupy)
• [Models] Extend PyFuncModel spec to support conda and virtualenv subfields (#6684, @harupy)
• [Scoring] Remove support for defining input formats using the Content-Type header (#6575, @tomasatdatabricks; #7254, @adriangonz)
• [Scoring] Replace the --no-conda CLI option argument for native serving with --env-manager='local' (#6501, @harupy)
• [Scoring] Remove public APIs for mlflow.sagemaker.deploy() and mlflow.sagemaker.delete() in favor of MLflow deployments APIs, such as mlflow deployments -t sagemaker (#6650, @dbczumar)
• [Scoring] Rename input argument df to inputs in mlflow.deployments.predict() method (#6681, @BenWilson2)
• [Projects] Replace the use_conda argument with the env_manager argument within the run CLI command for MLflow Projects (#6654, @harupy)
• [Projects] Modify the MLflow Projects docker image build options by renaming --skip-image-build to --build-image with a default of False (#7011, @harupy)
• [Integrations/Azure] Remove deprecated mlflow.azureml modules from MLflow in favor of the azure-mlflow deployment plugin (#6691, @BenWilson2)
• [R] Remove conda integration with the R client (#6638, @harupy)

Bug fixes:

• [Recipes] Fix rendering issue with profile cards polyfill (#7154, @hubertzub-db)
• [Tracking] Set the MLflow Run name correctly when specified as part of the tags argument to mlflow.start_run() (#7228, @Cokral)
• [Tracking] Fix an issue with conflicting MLflow Run name assignment if the mlflow.runName tag is set (#7138, @harupy)
• [Scoring] Fix incorrect payload constructor error in SageMaker deployment client predict() API (#7193, @dbczumar)
• [Scoring] Fix an issue where DataCaptureConfig information was not preserved when updating a Sagemaker deployment (#7281, @harupy)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 1.30.0!

MLflow 1.30.0 includes several major features and improvements

Features:

• [Pipelines] Introduce hyperparameter tuning support to MLflow Pipelines (#6859, @prithvikannan)
• [Pipelines] Introduce support for prediction outlier comparison to training data set (#6991, @jinzhang21)
• [Pipelines] Introduce support for recording all training parameters for reproducibility (#7026, #7094, @prithvikannan)
• [Pipelines] Add support for Delta tables as a datasource in the ingest step (#7010, @sunishsheth2009)
• [Pipelines] Add expanded support for data profiling up to 10,000 columns (#7035, @prithvikanna)
• [Pipelines] Add support for AutoML in MLflow Pipelines using FLAML (#6959, @mshtelma)
• [Pipelines] Add support for simplified transform step execution by allowing for unspecified configuration (#6909, @apurva-koti)
• [Pipelines] Introduce a data preview tab to the transform step card (#7033, @prithvikannan)
• [Tracking] Introduce run_name attribute for create_run, get_run and update_run APIs (#6782, #6798 @apurva-koti)
• [Tracking] Add support for searching by creation_time and last_update_time for the search_experiments API (#6979, @harupy)
• [Tracking] Add support for search terms run_id IN and run ID NOT IN for the search_runs API (#6945, @harupy)
• [Tracking] Add support for searching by user_id and end_time for the search_runs API (#6881, #6880 @subramaniam02)
• [Tracking] Add support for searching by run_name and run_id for the search_runs API (#6899, @harupy; #6952, @alexacole)
• [Tracking] Add support for synchronizing run name attribute and mlflow.runName tag (#6971, @BenWilson2)
• [Tracking] Add support for signed tracking server requests using AWSSigv4 and AWS IAM (#7044, @pdifranc)
• [Tracking] Introduce the update_run() API for modifying the status and name attributes of existing runs (#7013, @gabrielfu)
• [Tracking] Add support for experiment deletion in the mlflow gc cli API (#6977, @shaikmoeed)
• [Models] Add support for environment restoration in the evaluate() API (#6728, @jerrylian-db)
• [Models] Remove restrictions on binary classification labels in the evaluate() API (#7077, @dbczumar)
• [Scoring] Add support for BooleanType to mlflow.pyfunc.spark_udf() (#6913, @BenWilson2)
• [SQLAlchemy] Add support for configurable Pool class options for SqlAlchemyStore (#6883, @mingyu89)

Bug fixes:

• [Pipelines] Enable Pipeline subprocess commands to create a new SparkSession if one does not exist (#6846, @prithvikannan)
• [Pipelines] Fix a rendering issue with bool column types in Step Card data profiles (#6907, @sunishsheth2009)
• [Pipelines] Add validation and an exception if required step files are missing (#7067, @mingyu89)
• [Pipelines] Change step configuration validation to only be performed during runtime execution of a step (#6967, @prithvikannan)
• [Tracking] Fix infinite recursion bug when inferring the model schema in mlflow.pyspark.ml.autolog() (#6831, @harupy)
• [UI] Remove the browser error notification when failing to fetch artifacts (#7001, @kevingreer)
• [Models] Allow mlflow-skinny package to serve as base requirement in MLmodel requirements (#6974, @BenWilson2)
• [Models] Fix an issue with code path resolution for loading SparkML models (#6968, @dbczumar)
• [Models] Fix an issue with dependency inference in logging SparkML models (#6912, @BenWilson2)
• [Models] Fix an issue involving potential duplicate downloads for SparkML models (#6903, @serena-ruan)
• [Models] Add missing pos_label to sklearn.metrics.precision_recall_curve in mlflow.evaluate() (#6854, @dbczumar)
• [SQLAlchemy] Fix a bug in SqlAlchemyStore where set_tag() updates the incorrect tags (#7027, @gabrielfu)

Documentation updates:

• [Models] Update details regarding the default Keras serialization format (#7022, @balvisio)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 1.29.0!

MLflow 1.29.0 includes several major features and improvements:

Features:

• [Pipelines] Improve performance and fidelity of dataset profiling in the scikit-learn regression Pipeline (#6792, @sunishsheth2009)
• [Pipelines] Add an mlflow pipelines get-artifact CLI for retrieving Pipeline artifacts (#6517, @prithvikannan)
• [Pipelines] Introduce an option for skipping dataset profiling to the scikit-learn regression Pipeline (#6456, @apurva-koti)
• [Pipelines / UI] Display an mlflow pipelines CLI command for reproducing a Pipeline run in the MLflow UI (#6376, @hubertzub-db)
• [Tracking] Automatically generate friendly names for Runs if not supplied by the user (#6736, @BenWilson2)
• [Tracking] Add load_text(), load_image() and load_dict() fluent APIs for convenient artifact loading (#6475, @subramaniam02)
• [Tracking] Add creation_time and last_update_time attributes to the Experiment class (#6756, @subramaniam02)
• [Tracking] Add official MLflow Tracking Server Dockerfiles to the MLflow repository (#6731, @oojo12)
• [Tracking] Add searchExperiments API to Java client and deprecate listExperiments (#6561, @dbczumar)
• [Tracking] Add mlflow_search_experiments API to R client and deprecate mlflow_list_experiments (#6576, @dbczumar)
• [UI] Make URLs clickable in the MLflow Tracking UI (#6526, @marijncv)
• [UI] Introduce support for csv data preview within the artifact viewer pane (#6567, @nnethery)
• [Model Registry / Models] Introduce mlflow.models.add_libraries_to_model() API for adding libraries to an MLflow Model (#6586, @arjundc-db)
• [Models] Add model validation support to mlflow.evaluate() (#6582, @zhe-db, @jerrylian-db)
• [Models] Introduce sample_weights support to mlflow.evaluate() (#6806, @dbczumar)
• [Models] Add pos_label support to mlflow.evaluate() for identifying the positive class (#6696, @harupy)
• [Models] Make the metric name prefix and dataset info configurable in mlflow.evaluate() (#6593, @dbczumar)
• [Models] Add utility for validating the compatibility of a dataset with a model signature (#6494, @serena-ruan)
• [Models] Add predict_proba() support to the pyfunc representation of scikit-learn models (#6631, @skylarbpayne)
• [Models] Add support for Decimal type inference to MLflow Model schemas (#6600, @shitaoli-db)
• [Models] Add new CLI command for generating Dockerfiles for model serving (#6591, @anuarkaliyev23)
• [Scoring] Add /health endpoint to scoring server (#6574, @gabriel-milan)
• [Scoring] Support specifying a variant_name during Sagemaker deployment (#6486, @nfarley-soaren)
• [Scoring] Support specifying a data_capture_config during SageMaker deployment (#6423, @jonwiggins)

Bug fixes:

• [Tracking] Make Run and Experiment deletion and restoration idempotent (#6641, @dbczumar)
• [UI] Fix an alignment bug affecting the Experiments list in the MLflow UI (#6569, @sunishsheth2009)
• [Models] Fix a regression in the directory path structure of logged Spark Models that occurred in MLflow 1.28.0 (#6683, @gwy1995)
• [Models] No longer reload the main module when loading model code (#6647, @Jooakim)
• [Artifacts] Fix an mlflow server compatibility issue with HDFS when running in --serve-artifacts mode (#6482, @shidianshifen)
• [Scoring] Fix an inference failure with 1-dimensional tensor inputs in TensorFlow and Keras (#6796, @LiamConnell)

Documentation updates:

• [Tracking] Mark the SearchExperiments API as stable (#6551, @dbczumar)
• [Tracking / Model Registry] Deprecate the ListExperiments, ListRegisteredModels, and list_run_infos() APIs (#6550, @dbczumar)
• [Scoring] Deprecate mlflow.sagemaker.deploy() in favor of SageMakerDeploymentClient.create() (#6651, @dbczumar)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 1.28.0!

MLflow 1.28.0 includes several major features and improvements:

Features:

• [Pipelines] Log the full Pipeline runtime configuration to MLflow Tracking during Pipeline execution (#6359, @jinzhang21)
• [Pipelines] Add pipeline.yaml configurations to specify the Model Registry backend used for model registration (#6284, @sunishsheth2009)
• [Pipelines] Support optionally skipping the transform step of the scikit-learn regression pipeline (#6362, @sunishsheth2009)
• [Pipelines] Add UI links to Runs and Models in Pipeline Step Cards on Databricks (#6294, @dbczumar)
• [Tracking] Introduce mlflow.search_experiments() API for searching experiments by name and by tags (#6333, @WeichenXu123; #6227, #6172, #6154, @harupy)
• [Tracking] Increase the maximum parameter value length supported by File and SQL backends to 500 characters (#6358, @johnyNJ)
• [Tracking] Introduce an --older-than flag to mlflow gc for removing runs based on deletion time (#6354, @Jason-CKY)
• [Tracking] Add MLFLOW_SQLALCHEMYSTORE_POOL_RECYCLE environment variable for recycling SQLAlchemy connections (#6344, @postrational)
• [UI] Display deeply nested runs in the Runs Table on the Experiment Page (#6065, @tospe)
• [UI] Add box plot visualization for metrics to the Compare Runs page (#6308, @ahlag)
• [UI] Display tags on the Compare Runs page (#6164, @CaioCavalcanti)
• [UI] Use scientific notation for axes when viewing metric plots in log scale (#6176, @RajezMariner)
• [UI] Add button to Metrics page for downloading metrics as CSV (#6048, @rafaelvp-db)
• [UI] Include NaN and +/- infinity values in plots on the Metrics page (#6422, @hubertzub-db)
• [Tracking / Model Registry] Introduce environment variables to control retry behavior and timeouts for REST API requests (#5745, @peterdhansen)
• [Tracking / Model Registry] Make MlflowClient importable as mlflow.MlflowClient (#6085, @subramaniam02)
• [Model Registry] Add support for searching registered models and model versions by tags (#6413, #6411, #6320, @WeichenXu123)
• [Model Registry] Add stage parameter to set_model_version_tag() (#6185, @subramaniam02)
• [Model Registry] Add --registry-store-uri flag to mlflow server for specifying the Model Registry backend URI (#6142, @Secbone)
• [Models] Improve performance of Spark Model logging on Databricks (#6282, @bbarnes52)
• [Models] Include Pandas Series names in inferred model schemas (#6361, @RynoXLI)
• [Scoring] Make model_uri optional in mlflow models build-docker to support building generic model serving images (#6302, @harupy)
• [R] Support logging of NA and NaN parameter values (#6263, @nathaneastwood)

Bug fixes and documentation updates:

• [Pipelines] Improve scikit-learn regression pipeline latency by limiting dataset profiling to the first 100 columns (#6297, @sunishsheth2009)
• [Pipelines] Use xdg-open instead of open for viewing Pipeline results on Linux systems (#6326, @strangiato)
• [Pipelines] Fix a bug that skipped Step Card rendering in Jupyter Notebooks (#6378, @apurva-koti)
• [Tracking] Use the 401 HTTP response code in authorization failure REST API responses, instead of 500 (#6106, @balvisio)
• [Tracking] Correctly classify artifacts as files and directories when using Azure Blob Storage (#6237, @nerdinand)
• [Tracking] Fix a bug in the File backend that caused run metadata to be lost in the event of a failed write (#6388, @dbczumar)
• [Tracking] Adjust mlflow.pyspark.ml.autolog() to only log model signatures for supported input / output data types (#6365, @harupy)
• [Tracking] Adjust mlflow.tensorflow.autolog() to log TensorFlow early stopping callback info when log_models=False is specified (#6170, @WeichenXu123)
• [Tracking] Fix signature and input example logging errors in mlflow.sklearn.autolog() for models containing transformers (#6230, @dbczumar)
• [Tracking] Fix a failure in mlflow gc that occurred when removing a run whose artifacts had been previously deleted (#6165, @dbczumar)
• [Tracking] Add missing sqlparse library to MLflow Skinny client, which is required for search support (#6174, @dbczumar)
• [Tracking / Model Registry] Fix an mlflow server bug that rejected parameters and tags with empty string values (#6179, @dbczumar)
• [Model Registry] Fix a failure preventing model version schemas from being downloaded with --serve-arifacts enabled (#6355, @abbas123456)
• [Scoring] Patch the Java Model Server to support MLflow Models logged on recent versions of the Databricks Runtime (#6337, @dbczumar)
• [Scoring] Verify that either the deployment name or endpoint is specified when invoking the mlflow deployments predict CLI (#6323, @dbczumar)
• [Scoring] Properly encode datetime columns when performing batch inference with mlflow.pyfunc.spark_udf() (#6244, @harupy)
• [Projects] Fix an issue where local directory paths were misclassified as Git URIs when running Projects (#6218, @ElefHead)
• [R] Fix metric logging behavior for +/- infinity values (#6271, @nathaneastwood)
• [Docs] Move Python API docs for MlflowClient from mlflow.tracking to mlflow.client (#6405, @dbczumar)
• [Docs] Document that MLflow Pipelines requires Make (#6216, @dbczumar)
• [Docs] Improve documentation for developing and testing MLflow JS changes in CONTRIBUTING.rst (#6330, @ahlag)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.

We are happy to announce the availability of MLflow 1.27.0!

MLflow 1.27.0 includes several major features and improvements:

• [Pipelines] With MLflow 1.27.0, we are excited to announce the release of MLflow Pipelines, an opinionated framework for structuring MLOps workflows that simplifies and standardizes machine learning application development and productionization. MLflow Pipelines makes it easy for data scientists to follow best practices for creating production-ready ML deliverables, allowing them to focus on developing excellent models. MLflow Pipelines also enables ML engineers and DevOps teams to seamlessly deploy models to production and incorporate them into applications. To get started with MLflow Pipelines, check out the docs at https://mlflow.org/docs/latest/pipelines.html. (#6115)

• [UI] Introduce UI support for searching and comparing runs across multiple Experiments (#5971, @r3stl355)

More features:

• [Tracking] When using batch logging APIs, automatically split large sets of metrics, tags, and params into multiple requests (#6052, @nzw0301)
• [Tracking] When an Experiment is deleted, SQL-based backends also move the associate Runs to the "deleted" lifecycle stage (#6064, @AdityaIyengar27)
• [Tracking] Add support for logging single-element ndarray and tensor instances as metrics via the mlflow.log_metric() API (#5756, @ntakouris)
• [Models] Add support for CatBoostRanker models to the mlflow.catboost flavor (#6032, @danielgafni)
• [Models] Integrate SHAP's KernelExplainer with mlflow.evaluate(), enabling model explanations on categorical data (#6044, #5920, @WeichenXu123)
• [Models] Extend mlflow.evaluate() to automatically log the score() outputs of scikit-learn models as metrics (#5935, #5903, @WeichenXu123)

Bug fixes and documentation updates:

• [UI] Fix broken model links in the Runs table on the MLflow Experiment Page (#6014, @hctpbl)
• [Tracking/Installation] Require sqlalchemy>=1.4.0 upon MLflow installation, which is necessary for usage of SQL-based MLflow Tracking backends (#6024, @sniafas)
• [Tracking] Fix a regression that caused mlflow server to reject LogParam API requests containing empty string values (#6031, @harupy)
• [Tracking] Fix a failure in scikit-learn autologging that occurred when matplotlib was not installed on the host system (#5995, @fa9r)
• [Tracking] Fix a failure in TensorFlow autologging that occurred when training models on tf.data.Dataset inputs (#6061, @dbczumar)
• [Artifacts] Address artifact download failures from SFTP locations that occurred due to mismanaged concurrency (#5840, @rsundqvist)
• [Models] Fix a bug where MLflow Models did not restore bundled code properly if multiple models use the same code module name (#5926, @BFAnas)
• [Models] Address an issue where mlflow.sklearn.model() did not properly restore bundled model code (#6037, @WeichenXu123)
• [Models] Fix a bug in mlflow.evaluate() that caused input data objects to be mutated when evaluating certain scikit-learn models (#6141, @dbczumar)
• [Models] Fix a failure in mlflow.pyfunc.spark_udf that occurred when the UDF was invoked on an empty RDD partition (#6063, @WeichenXu123)
• [Models] Fix a failure in mlflow models build-docker that occurred when env-manager=local was specified (#6046, @bneijt)
• [Projects] Improve robustness of the git repository check that occurs prior to MLflow Project execution (#6000, @dkapur17)
• [Projects] Address a failure that arose when running a Project that does not have a master branch (#5889, @harupy)
• [Docs] Correct several typos throughout the MLflow docs (#5959, @ryanrussell)

For a comprehensive list of changes, see the release change log, and check out the latest documentation on mlflow.org.