Accelerated Data Science 2.6.3 is released

The following changes were made in this version.

  • Added prepare_save_deploy() method to the GenericModel class. Now you can prepare model artifacts and deploy the model within one command.
  •  Added support for binary payloads in model deployment.
  • Updated AutoMLModel, GenericModelLightgbmModel, PyTorchModel, SklearnModelTensorflowModel, and XgboostModel classes to support binary payloads in model deployment.
  • To limit job runtime, added the with_maximum_runtime_in_minutes() method in the CondaRuntime, DataFlowNotebookRuntime, DataFlowRuntime, GitPythonRuntime, NotebookRuntime, and ScriptRuntime classes.
  • Deprecated the ads.dataflow.DataFlow class. Use the class instead.
  • The class supports published conda environments.

For more information, see Data ScienceADS SDK, and ocifs SDK. Take a look at our Data Science blog.