MLflow 2.3.0 includes several major features and improvements

Features:

• [Models] Introduce a new transformers named flavor (#8236, #8181, #8086, @BenWilson2)
• [Models] Introduce a new openai named flavor (#8191, #8155, @harupy)
• [Models] Introduce a new langchain named flavor (#8251, #8197, @liangz1, @sunishsheth2009)
• [Models] Add support for Pytorch and Lightning 2.0 (#8072, @shrinath-suresh)
• [Tracking] Add support for logging LLM input, output, and prompt artifacts (#8234, #8204, @sunishsheth2009)
• [Tracking] Add support for HTTP Basic Auth in the MLflow tracking server (#8130, @gabrielfu)
• [Tracking] Add support for search_model_versions to high-level fluent API (#8223, @mariusschlegel)
• [Artifacts] Add support for parallelized artifact downloads (#8116, @apurva-koti)
• [Artifacts] Add support for parallelized artifact uploads for AWS (#8003, @harupy)
• [Artifacts] Add content type headers to artifact upload requests for the HttpArtifactRepository (#8048, @WillEngler)
• [Model Registry] Added alias support to MLflow client (#8164, #8094, #8055 @arpitjasa-db)
• [UI] Add support for custom domain git providers (#7933, @gusghrlrl101)
• [Scoring] Add plugin support for customization of MLflow serving endpoints (#7757, @jmahlik)
• [Scoring] Add support to MLflow serving that allows configuration of multiple inference workers (#8035, @M4nouel)
• [Sagemaker] Add support for asynchronous inference configuration on Sagemaker (#8009, @thomasbell1985)
• [Build] Remove shap as a core dependency of MLflow (#8199, @jmahlik)

Bug fixes:

• [Models] Fix a bug with tensorflow autologging for models with multiple inputs (#8097, @jaume-ferrarons)
• [Recipes] Fix a bug with Pandas 2.0 updates for profiler rendering of datetime types (#7925, @sunishsheth2009)
• [Tracking] Prevent exceptions from being raised if a parameter is logged with an existing key whose value is identical to the logged parameter (#8038, @AdamStelmaszczyk)
• [Tracking] Fix an issue with deleting experiments in the FileStore backend (#8178, @mariusschlegel)
• [Tracking] Fix a UI bug where the "Source Run" field in the Model Version page points to an incorrect set of artifacts (#8156, @WeichenXu123)
• [Tracking] Fix a bug wherein renaming a run reverts its current lifecycle status to UNFINISHED (#8154, @WeichenXu123)
• [Tracking] Fix a bug where a file URI could be used as a model version source (#8126, @harupy)
• [Projects] Fix an issue with MLflow projects that have submodules contained within a project (#8050, @kota-iizuka)
• [Examples] Fix lightning hyperparameter tuning examples (#8039, @BenWilson2)
• [Server-infra] Fix bug with Cache-Control headers for static server files (#8016, @jmahlik)

Documentation updates:

• [Examples] Add a new and thorough example for the creation of custom model flavors (#7867, @benjaminbluhm)

Small bug fixes and documentation updates:

#8262, #8252, #8250, #8228, #8221, #8203, #8134, #8040, #7994, #7934, @BenWilson2; #8258, #8255, #8253, #8248, #8247, #8245, #8243, #8246, #8244, #8242, #8240, #8229, #8198, #8192, #8112, #8165, #8158, #8152, #8148, #8144, #8143, #8120, #8107, #8105, #8102, #8088, #8089, #8096, #8075, #8073, #8076, #8063, #8064, #8033, #8024, #8023, #8021, #8015, #8005, #7982, #8002, #7987, #7981, #7968, #7931, #7930, #7929, #7917, #7918, #7916, #7914, #7913, @harupy; #7955, @arjundc-db; #8219, #8110, #8093, #8087, #8091, #8092, #8029, #8028, #8031, @jerrylian-db; #8187, @apurva-koti; #8210, #8001, #8000, @arpitjasa-db; #8161, #8127, #8095, #8090, #8068, #8043, #7940, #7924, #7923, @dbczumar; #8147, @morelen17; #8106, @WeichenXu123; #8117, @eltociear; #8100, @laerciop; #8080, @elado; #8070, @grofte; #8066, @yukimori; #8027, #7998, @liangz1; #7999, @martlaf; #7964, @viditjain99; #7928, @alekseyolg; #7909, #7901, #7844, @smurching; #7971, @n30111; #8012, @mingyu89; #8137, @lobrien; #7992, @robmarkcole; #8263, @sunishsheth2009

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)

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

• [Model Registry] Fix a bug that caused too many results to be requested by default when calling MlflowClient.search_model_versions() (#7935, @dbczumar)
• [Model Registry] Patch for GHSA-xg73-94fp-g449 (#7908, @harupy)
• [Model Registry] Patch for GHSA-wp72-7hj9-5265 (#7965, @harupy)

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)

Small bug fixes and documentation updates:

#7866, #7751, #7724, #7699, #7697, #7666, @alekseyolg; #7896, #7861, #7858, #7862, #7872, #7859, #7863, #7767, #7766, #7765, #7741, @smurching; #7895, #7877, @viditjain99; #7898, @midhun1998; #7891, #7892, #7886, #7882, #7883, #7875, #7874, #7871, #7868, #7854, #7847, #7845, #7838, #7830, #7837, #7836, #7834, #7831, #7828, #7825, #7826, #7824, #7823, #7778, #7780, #7776, #7775, #7773, #7772, #7769, #7756, #7768, #7764, #7685, #7726, #7722, #7720, #7423, #7712, #7710, #7713, #7688, #7663, #7674, #7673, #7672, #7662, #7653, #7646, #7615, #7614, #7586, #7601, #7598, #7602, #7599, #7577, #7585, #7583, #7584, @harupy; #7865, #7803, #7753, #7719, @dipanjank; #7796, @serena-ruan; #7849, @turbotimon; #7822, #7600, @WeichenXu123; #7811, @guyrosin; #7812, #7788, #7787, #7748, #7730, #7616, #7593, @dbczumar; #7793, @Joel-hanson; #7792, #7694, #7643, @BenWilson2; #7771, #7657, #7644, @nsenno-dbr; #7738, @wkrt7; #7740, @Ark-kun; #7739, #7733, @bali0019; #7723, @andrehp; #7691, #7582, @agoyot; #7721, @Eseeldur; #7709, @srowen; #7693, @ry3s; #7649, @funkypenguin; #7665, @benjaminbluhm; #7668, @eltociear; #7550, @danielhstahl; #7920, @arjundc-db

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)

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)

Small bug fixes and documentation updates:

#7571, #7543, #7529, #7435, #7399, @WeichenXu123; #7568, @xiaoye-hua; #7549, #7557, #7509, #7498, #7499, #7485, #7486, #7484, #7391, #7388, #7390, #7381, #7366, #7348, #7346, #7334, #7340, #7323, @BenWilson2; #7561, #7562, #7560, #7553, #7546, #7539, #7544, #7542, #7541, #7533, #7507, #7470, #7469, #7467, #7466, #7464, #7453, #7449, #7450, #7440, #7430, #7436, #7429, #7426, #7410, #7406, #7409, #7407, #7405, #7396, #7393, #7395, #7384, #7376, #7379, #7375, #7354, #7353, #7351, #7352, #7350, #7345, #6493, #7343, #7344, @harupy; #7494, @dependabot[bot]; #7526, @tobycheese; #7489, @liangz1; #7534, @Jingnan-Jia; #7496, @danielhstahl; #7504, #7503, #7459, #7454, #7447, @tsugumi-sys; #7461, @wkrt7; #7451, #7414, #7372, #7289, @sunishsheth2009; #7441, @ikrizanic; #7432, @Pochingto; #7386, @jhallard; #7370, #7373, #7371, #7336, #7341, #7342, @dbczumar; #7335, @prithvikannan

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. 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)

Small bug fixes and documentation updates:

#7309, #7314, #7288, #7276, #7244, #7207, #7175, #7107, @sunishsheth2009; #7261, #7313, #7311, #7249, #7278, #7260, #7284, #7283, #7263, #7266, #7264, #7267, #7265, #7250, #7259, #7247, #7242, #7143, #7214, #7226, #7230, #7227, #7229, #7225, #7224, #7223, #7210, #7192, #7197, #7196, #7204, #7198, #7191, #7189, #7184, #7182, #7170, #7183, #7131, #7165, #7151, #7164, #7168, #7150, #7128, #7028, #7118, #7117, #7102, #7072, #7103, #7101, #7100, #7099, #7098, #7041, #7040, #6978, #6768, #6719, #6669, #6658, #6656, #6655, #6538, #6507, #6504 @harupy; #7310, #7308, #7300, #7290, #7239, #7220, #7127, #7091, #6713 @BenWilson2; #7299, #7271, #7209, #7180, #7179, #7158, #7147, #7114, @prithvikannan; #7275, #7245, #7134, #7059, @jinzhang21; #7306, #7298, #7287, #7272, #7258, #7236, @ayushthe1; #7279, @tk1012; #7219, @rddefauw; #7218, #7208, #7188, #7190, #7176, #7137, #7136, #7130, #7124, #7079, #7052, #6541 @dbczumar; #6640, @WeichenXu123; #7200, @hubertzub-db; #7121, @Gonmeso; #6988, @alonisser; #7141, @pdifranc; #7086, @jerrylian-db; #7286, @shogohida

The first release candidate for MLflow 2.0.

Installation

# Make sure python version is >=3.8
pip install mlflow==2.0.0rc0

Documentation

MLflow 2.0.0rc0 documentation

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)

Small bug fixes and documentation updates:

#7093, #7095, #7092, #7064, #7049, #6921, #6920, #6940, #6926, #6923, #6862, @jerrylian-db; #6946, #6954, #6938, @mingyu89; #7047, #7087, #7056, #6936, #6925, #6892, #6860, #6828, @sunishsheth2009; #7061, #7058, #7098, #7071, #7073, #7057, #7038, #7029, #6918, #6993, #6944, #6976, #6960, #6933, #6943, #6941, #6900, #6901, #6898, #6890, #6888, #6886, #6887, #6885, #6884, #6849, #6835, #6834, @harupy; #7094, #7065, #7053, #7026, #7034, #7021, #7020, #6999, #6998, #6996, #6990, #6989, #6934, #6924, #6896, #6895, #6876, #6875, #6861, @prithvikannan; #7081, #7030, #7031, #6965, #6750, @bbarnes52; #7080, #7069, #7051, #7039, #7012, #7004, @dbczumar; #7054, @jinzhang21; #7055, #7037, #7036, #6949, #6951, @apurva-koti; #6815, @michaguenther; #6897, @chaturvedakash; #7025, #6981, #6950, #6948, #6937, #6829, #6830, @BenWilson2; #6982, @vadim; #6985, #6927, @kriscon-db; #6917, #6919, #6872, #6855, @WeichenXu123; #6980, @utkarsh867; #6973, #6935, @wentinghu; #6930, @mingyangge-db; #6956, @RohanBha1; #6916, @av-maslov; #6824, @shrinath-suresh; #6732, @oojo12; #6807, @ikrizanic; #7066, @subramaniam20jan; #7043, @AvikantSrivastava; #6879, @jspablo

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)
Small bug fixes and documentation updates:

#6803, #6804, #6801, #6791, #6772, #6745, #6762, #6760, #6761, #6741, #6725, #6720, #6666, #6708, #6717, #6704, #6711, #6710, #6706, #6699, #6700, #6702, #6701, #6685, #6664, #6644, #6653, #6629, #6639, #6624, #6565, #6558, #6557, #6552, #6549, #6534, #6533, #6516, #6514, #6506, #6509, #6505, #6492, #6490, #6478, #6481, #6464, #6463, #6460, #6461, @harupy; #6810, #6809, #6727, #6648, @BenWilson2; #6808, #6766, #6729, @jerrylian-db; #6781, #6694, @marijncv; #6580, #6661, @bbarnes52; #6778, #6687, #6623, @shraddhafalane; #6662, #6737, #6612, #6595, @sunishsheth2009; #6777, @aviralsharma07; #6665, #6743, #6573, @liangz1; #6784, @apurva-koti; #6753, #6751, @mingyu89; #6690, #6455, #6484, @kriscon-db; #6465, #6689, @hubertzub-db; #6721, @WeichenXu123; #6722, #6718, #6668, #6663, #6621, #6547, #6508, #6474, #6452, @dbczumar; #6555, #6584, #6543, #6542, #6521, @dsgibbons; #6634, #6596, #6563, #6495, @prithvikannan; #6571, @smurching; #6630, #6483, @serena-ruan; #6642, @thinkall; #6614, #6597, @jinzhang21; #6457, @cnphil; #6570, #6559, @kumaryogesh17; #6560, #6540, @iamthen0ise; #6544, @monkero; #6438, @ahlag; #3292, @dolfinus; #6637, @ninabacc-db; #6632, @arpitjasa-db