CRAN Release

New features

• Add foreach backend for distributed training and batch inferencing. (#215)
• Add R Section in Environment Definition.
• Add Azure Data Lake Gen2 Datastore support as an experimental feature.(#279)
• Add github_package() and cran_package() constructors to specify packages and
  package versions in Estimator and Environment Definition.(#310)
• Expose query_timeout parameter for create_tabular_dataset_from_sql_query().(#308)
• Add data_path() so that Dataset constructors can take in DataPath objects. (#271)
• Add dataset_consumption_config(). (#272)
• Add support for ResourceConfiguration and registering models from run.(#300)
• Expose cluster_purpose param for create_aks_compute(). (#276)
• Add interactive_login_authentication() and service_principal_authentication. (#263) (#241)
• Deprecate live run widget.

Bug fixes

• Fix issues with Dataset creation and usage.
• Fix Interactive Authentication.

Documentation

• Add "Troubleshooting" article.
• Add "Deploying models" vignette.
• Add sample for batch inferencing with foreach backend.
• Make all vignettes discoverable via CRAN. (#320)

CRAN Release

New features

• Methods for creating and managing Azure ML Datasets.
• Update create_workspace() to use sku parameter.
• Expose file_name parameter to load_workspace_from_config().
• v2 of the Azure ML run details widget in RStudio Viewer pane.

Bug fixes

• Fix installation issue introduced by latest reticulate 1.14 release.
• Fix default CRAN CDN.
• Remove dependency on DAAG package in train-and-deploy-to-aci vignette.

CRAN Release

Initial CRAN release

Initial features

• Methods for creating and managing Azure Machine Learning (Azure ML) Workspaces.
• Methods for registering and managing Azure ML Datastores.
• Methods for managing secrets in the Azure Key Vault associated with a workspace.
• Methods for creating and managing Azure Machine Learning Compute (AmlCompute).
• Methods for creating, managing, and attaching Azure Kubernetes Service clusters as compute targets (AksCompute).
• Methods for creating and managing Azure ML Environments for training and deployment.
• Methods for creating and submitting Azure ML Experiments.
• Methods for configuring and managing Azure ML Runs.
• Methods for logging metrics to Azure ML during runs.
• Methods for configuring and managing Azure ML hyperparameter tuning runs.
• Methods for model registration and management to Azure ML.
• Methods for deploying models as local webservices for testing.
• Methods for deploying models as webservices on Azure Container Instances (ACI) and Azure Kubernetes Service (AKS).
• view_run_details() for invoking remote Azure ML run details in RStudio Viewer pane.

Documentation

• Vignettes for installation, TensorFlow training on AmlCompute, hyperparameter tuning a Keras model with Azure ML's HyperDrive service, and production deloying a model as a webservice to AKS.
• Additional code samples for setting up workspaces, training on AmlCompute, and deploying a model as a webservice to ACI.

v0.5.6

Release for Ignite

v0.5.2

Update R SDK docs via Build 20191015.4 [skip ci]

v0.4.0

Initial release for AzureML R SDK. R users are able to train and deploy their models using the Azure ML R SDK, while leveraging additional functionality including run tracking and hyperparameter tuning.