Accelerated Data Science 2.8.4 is released

The following changes were made in ADS 2.8.4:

  • Added support for creating ADSDataset from pandas dataframe.
  • Added support for multi-model deployment using Triton.
  • Added support for model deployment local testing in ads opctl CLI.
  • Added support in ads opctl CLI to generate starter YAML specification for the Data Science job, Data Flow application, Data Science model deployment, and pipeline resources.
  • Added support for invoking model prediction locally with predict(local=True).
  • Added support for attaching customized score.py when preparing model.
  • Added status check for model deployment delete, activate, or deactivate APIs.
  • Added support for training and verifying SparkPipelineModel in Data Flow.
  • Added support for generating score.py for GPU model deployment.
  • Added support for setting defined tags in Data Science jobs.
  • Improved model deployment progress bar.
  • Fixed bug when using ads opctl CLI to run jobs locally.
  • Fixed bug in Dataflow magic when using archive_uri in Data Flow configuration.

For more information, see Data Science and take a look at our Data Science blog.