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MLflow 1.9.0

· One min read
MLflow maintainers

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.