- 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)
cran_package() constructors to specify packages and package versions in Estimator and Environment Definition.(#310)
query_timeout parameter for
data_path() so that Dataset constructors can take in DataPath objects. (#271)
- Add support for ResourceConfiguration and registering models from run.(#300)
cluster_purpose param for
service_principal_authentication. (#263) (#241)
- Deprecate live run widget.
- Fix issues with Dataset creation and usage.
- Fix Interactive Authentication.
- Add “Troubleshooting” article.
- Add “Deploying models” vignette.
- Add sample for batch inferencing with foreach backend.
- Make all vignettes discoverable via CRAN. (#320)
- Methods for creating and managing Azure ML Datasets.
create_workspace() to use
file_name parameter to
- v2 of the Azure ML run details widget in RStudio Viewer pane.
- 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.
view_run_details for invoking Azure ML run details widget with live updates in RStudio Viewer pane.
Initial CRAN release
- 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.
- 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.