Mlflow

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; 7332, 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; 7333, 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

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

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, 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

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)

Small bug fixes and doc updates (6322, 6321, 6213, KarthikKothareddy; 6409, 6408, 6396, 6402, 6399, 6398, 6397, 6390, 6381, 6386, 6385, 6373, 6375, 6380, 6374, 6372, 6363, 6353, 6352, 6350, 6351, 6349, 6347, 6287, 6341, 6342, 6340, 6338, 6319, 6314, 6316, 6317, 6318, 6315, 6313, 6311, 6300, 6292, 6291, 6289, 6290, 6278, 6279, 6276, 6272, 6252, 6243, 6250, 6242, 6241, 6240, 6224, 6220, 6208, 6219, 6207, 6171, 6206, 6199, 6196, 6191, 6190, 6175, 6167, 6161, 6160, 6153, harupy; 6193, jwgwalton; 6304, 6239, 6234, 6229, sunishsheth2009; 6258, xanderwebs; 6106, balvisio; 6303, bbarnes52; 6117, wenfeiy-db; 6389, 6214, apurva-koti; 6412, 6420, 6277, 6266, 6260, 6148, WeichenXu123; 6120, ameya-parab; 6281, nathaneastwood; 6426, 6415, 6417, 6418, 6257, 6182, 6157, dbczumar; 6189, shrinath-suresh; 6309, SamirPS; 5897, temporaer; 6251, herrmann; 6198, sniafas; 6368, 6158, jinzhang21; 6236, subramaniam02; 6036, serena-ruan; 6430, ninabacc-db)

- [**Pipelines**] With MLflow 1.27.0, we are excited to announce the release of
[**MLflow Pipelines**](https://mlflow.org/docs/latest/pipelines.html), 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)

Small bug fixes and doc updates (6041, drsantos89; 6138, 6137, 6132, sunishsheth2009; 6144, 6124, 6125, 6123, 6057, 6060, 6050, 6038, 6029, 6030, 6025, 6018, 6019, 5962, 5974, 5972, 5957, 5947, 5907, 5938, 5906, 5932, 5919, 5914, 5888, 5890, 5886, 5873, 5865, 5843, harupy; 6113, comojin1994; 5930, yashaswikakumanu; 5837, shrinath-suresh; 6067, deepyaman; 5997, idlefella; 6021, BenWilson2; 5984, Sumanth077; 5929, krunal16-c; 5879, kugland; 5875, ognis1205; 6006, ryanrussell; 6140, jinzhang21; 5983, elk15; 6022, apurva-koti; 5982, EB-Joel; 5981, 5980, punitkashyup; 6103, ikrizanic; 5988, 5969, SaumyaBhushan; 6020, 5991, WeichenXu123; 5910, 5912, Dark-Knight11; 6005, Asinsa; 6023, subramaniam02; 5999, Regis-Caelum; 6007, CaioCavalcanti; 5943, kvaithin; 6017, 6002, NeoKish; 6111, T1b4lt; 5986, seyyidibrahimgulec; 6053, Zohair-coder; 6146, 6145, 6143, 6139, 6134, 6136, 6135, 6133, 6071, 6070, dbczumar; 6026, rotate2050)

- [Installation] Fix compatibility issue with `protobuf >= 4.21.0` (5945, harupy)
- [Models] Fix `get_model_dependencies` behavior for `models:` URIs containing artifact paths (5921, harupy)
- [Models] Revert a problematic change to `artifacts` persistence in `mlflow.pyfunc.log_model()` that was introduced in MLflow 1.25.0 (5891, kyle-jarvis)
- [Models] Close associated image files when `EvaluationArtifact` outputs from `mlflow.evaluate()` are garbage collected (5900, WeichenXu123)

Small bug fixes and updates (5874, 5942, 5941, 5940, 5938, harupy; 5893, PrajwalBorkar; 5909, yashaswikakumanu; 5937, BenWilson2)