The get_azure_token function | Authentication methods | Managed identities | Inside a web app | Authentication vs authorization: | Caching | Refreshing | More information

Interactive authentication on a local machine | Interactive authentication in a remote session | Interactive authentication in a webapp | Non-interactive authentication | More information

Authenticate as a user | Create an app registration | Set the app permissions | Give users a role assignment in the storage account | Authenticating | Authenticate as the application | Create the app registration and give it a client secret | Give the app's service principal a role assignment in the storage account

Storage endpoints | Listing, creating and deleting containers | Files and blobs | Uploading and downloading | Parallel connections | File format helpers | Transfer to and from connections | Copy from URLs (blob storage only) | Appending (blob storage only) | Interface to AzCopy | Other features | Metadata | Admin interface

Authentication | Subscriptions and resource groups | Resources and templates | Common methods | Tagging | Locking | Role-based access control | Conclusion

Resource Manager interface | Client interface

Authenticating with a service principal from ACI to ACR | Authenticating with a service principal from AKS to ACR | Creating an AKS resource and reusing an existing service principal | Integrating AKS with Azure Active Directory

Model object and image | Deploy to AKS | Install traefik | Assign domain name | Generate an auth file | Deploy the service | Call the service | Further comments

Resource Manager interface | Client interface | Keys | Secrets | Certificates | Storage accounts | See also

Service principal | App registration | Application permissions and security | Sample code skeleton | Service account | Creating a service account | Sample code

Authentication | Using the device code flow | Specifying the tenant | App registration and approvals | Using your own app registration | Using other app registrations: last-resort workarounds | Authenticating with a token | Other issues

Interactive authentication | Non-interactive authentication | Creating a custom app registration | Set the app role and scope

Template helper functions

Interactive authentication | Non-interactive authentication | Creating a custom app registration | Set the app permissions

Extend the ms_object base class | Register the class with register_graph_class | Add getter and setter methods

Authentication | Users and groups | Registered apps and service principals | Common methods | See also

App registration | Shiny code skeleton

OneDrive | SharePoint

dplyr Interface | Exporting to storage | DBI interface

Channels | Channel messages | Channel files | Private channels | Chats

Fit the model | Scoring script for plumber | Create a Dockerfile | Build and upload the image | Deploy to an Azure Container Instance | Deploy to a Kubernetes cluster | Security note | See also

Batching | Paging

Outlook client | Writing emails | Attachments | Other methods | Managing your emails | Replying to an email | Working with folders

Installation | Compute instance | Older version of azuremlsdk | Error: "Unable to install packages" | Training | "ModuleNotFoundError": "No module named 'ruamel'" | "PermissionError" when submitting an experiment in RStudio | "Error": "C stack usage ______________ is too close to the limit" | Deployment | Resource quotas

AzureCosmosR | SQL interface | Other client interfaces | MongoDB | Table storage | ODBC (SQL interface) | Azure Resource Manager interface | Further information

Azure table storage | Tables | Entities | Batch transactions

Creating the resources | Training | Creating the project | Adding and tagging images | Training the model | Deployment | Publishing to a prediction resource | Exporting as standalone | See also

Load the data | Try out several models | Select a model for deployment | Deploy a model to Azure Container Instances | Use the prediction service in a Shiny application

Register your model | Register a model from an Azure ML run | Register a model from local file(s) | Choose a compute target | Prepare deloyment artifacts | 1. Define your entry script and dependencies | Locate model files in your entry script | Example entry script | 2. Define your inference environment | 3. Define your deployment configuration | Deploy to target | 1. Local deployment | 2. ACI deployment | 3. AKS deployment | Troubleshooting deployment | Web service authentication | Key-based authentication | Token-based authentication | Consume web service | Update web service | Clean up resources | Additional references

What are hyperparameters? | Prerequisites | Set up development environment | Import azuremlsdk package | Load your workspace | Create an experiment | Create a compute target | Prepare the training script | Create an estimator | Configure the HyperDrive run | Define search space | Define termination policy | Finalize configuration | Submit the HyperDrive run | Analyse runs by performance | Clean up resources

Prerequisites | Set up development environment | Import azuremlsdk package | Load your workspace | Create an experiment | Create a compute target | Prepare the training script | Create an estimator | Submit the job | View run metrics | Clean up resources

Prerequisites | Writing the Dockerfile | Choose the base image | Specify conda dependencies | Specify R packages | Build and publish the image | Publish on Docker Hub | Publish on Azure Container Registry | Create an Azure ML Environment | Use the custom image for training | Use the custom image for deployment | Additional references

Install Conda | Install the azuremlsdk R package | Install the Azure ML Python SDK | Test installation | Troubleshooting

Prerequisites | Set up your testing environment | Import package | Load your workspace | Register the model | Provision an AKS cluster | Deploy as a web service | Define the inference dependencies | Deploy to AKS | Test the deployed service | Web service authentication | Key-based authentication | Token-based authentication | Clean up resources

Prerequisites | Set up your development environment | Install required packages | Load your workspace | Create an experiment | Create a compute target | Prepare data for training | Upload data to the datastore | Train a model | Prepare the training script | Create an estimator | Submit the job on the remote cluster | Retrieve training results | Get the logged metrics | Get the trained model | Deploy as a web service | Register the model | Define the inference dependencies | Deploy to ACI | Test the deployed service | Clean up resources

Deployment artifacts | Model object | TLS certificate/private key | App | Dockerfile | Create the container

What is an Azure ML workspace? | Setup | Azure subscription | Azure ML SDK installation | Configure your workspace | Workspace parameters | Create a new workspace | Access an existing workspace | Authenticate a workspace

Virtual machines | VM scalesets | Sharing resources

Creating the resources | Client interface | Sample images | Calls | analyze | describe | detect_objects | area_of_interest | tag | categorize | read_text | make_thumbnail | See also

Working with the pool

Storage endpoints and containers | Files and blobs

Subclass resource/template classes | Add accessor functions | Adding documentation | Add client-facing interface