We are happy to announce the availability of MLflow 1.9.0!

In addition to bug and documentation fixes, MLflow 1.9.0 includes the following major features and improvements:

  • log_model and save_model APIs now support saving model signatures (the model’s input and output schema) and example input along with the model itself (#2698, #2775, @tomasatdatabricks). Model signatures are used to reorder and validate input fields when scoring/serving models using the pyfunc flavor, mlflow models CLI commands, or mlflow.pyfunc.spark_udf (#2920, @tomasatdatabricks and @aarondav)
  • Introduce fastai model persistence and autologging APIs under mlflow.fastai (#2619, #2689 @antoniomdk)
  • Add pluggable mlflow.deployments API and CLI for deploying models to custom serving tools, e.g. RedisAI (#2327, @hhsecond)
  • Add plugin interface for executing MLflow projects against custom backends (#2566, @jdlesage)
  • Enable viewing PDFs logged as artifacts from the runs UI (#2859, @ankmathur96)
  • Significant performance and scalability improvements to metric comparison and scatter plots in the UI (#2447, @mjlbach)

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