2.5.9

ADS

• Added framework specific model serialization to add more inputs to the generated score.py file.

• Added the following framework-specific model classes:

  • AutoMLModel
  • SKlearnModel
  • XGBoostModel
  • LightGBMModel
  • PyTorchModel
  • TensorFlowModel

• For any framework not included in the preceding list, added another class:

  • GenericModel

• These model classes include methods specific to the frameworks that improve deployment speed. Some example methods are:

  • Prepare (the artifacts)
  • Save (metadata and model to model catalog)
  • Deploy (the models quickly with this method)
  • Predict (perform inference operations)

• Added support to create jobs with managed egress.

• Shortened the time for streaming large number of logs for job run logging.

2.5.8

ADS

• Fixed bug in automatic extraction of taxonomy metadata for Sklearn models.
• Fixed bug in jobs NotebookRuntime when using non-ASCII encoding.
• Added compatibility with Python 3.8 and 3.9.
• Added an enhanced string class, called ADSString. It adds functionality such as regular expression (RegEx) matching, and natural language processing (NLP) parsing. The class can be expanded by registering custom plugins to perform custom string processing actions.

2.5.7

ADS

• Fixed bug in DataFlow Job creation.
• Fixed bug in ADSDataset get_recommendations raising HTML is not defined exception.
• Fixed bug in jobs ScriptRuntime causing the parent artifact folder to be zipped and uploaded instead of the specified folder.
• Fixed bug in ModelDeployment raising TypeError exception when updating an existing model deployment.