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