Interface to the 'Azure Machine Learning' 'SDK'
Log a vector metric value to a run
Log a metric to a run
Log a predictions metric to a run
Log a residuals metric to a run
Log a row metric to a run
Log a table metric to a run
Specify a normal distribution of the form exp(normal(mu, sigma))
Specify a log uniform distribution
Create a deployment config for deploying an ACI web service
Create a deployment config for deploying an AKS web service
Attach an existing AKS cluster to a workspace
azureml module User can access functions/modules in azureml that are n...
Define a Bandit policy for early termination of HyperDrive runs
Define Bayesian sampling over a hyperparameter search space
Cancel a run
Specify a discrete set of options to sample from
Mark a run as completed.
Specify Azure Container Registry details
Convert the current dataset into a FileDataset containing CSV files.
Convert the current dataset into a FileDataset containing Parquet file...
Specifies a CRAN package to install in environment
Create an AksCompute cluster
Create an AmlCompute cluster
Create a child run
Create one or many child runs
Create a FileDataset to represent file streams.
Create an unregistered, in-memory Dataset from delimited files.
Create a TabularDataset to represent tabular data in JSON Lines files ...
Create an unregistered, in-memory Dataset from parquet files.
Create a TabularDataset to represent tabular data in SQL databases.
Create a new Azure Machine Learning workspace
Represents a path to data in a datastore.
Configure conversion to bool.
Configure conversion to datetime.
Configure conversion to 53-bit double.
Configure conversion to 64-bit integer.
Configure conversion to string.
Represent how to deliver the dataset to a compute target.
Define timestamp columns for the dataset.
Delete a cluster
Delete a local web service from the local machine
Delete a model from its associated workspace
Delete secrets from a keyvault
Delete a web service from a given workspace
Delete a workspace
Deploy a web service from registered model(s)
Detach an AksCompute cluster from its associated workspace
Download a file from a run
Download files from a run
Download data from a datastore to the local file system
Download file streams defined by the dataset as local files.
Download a model to the local file system
Drop the specified columns from the dataset.
Create an estimator
Create an Azure Machine Learning experiment
Filter Tabular Dataset with time stamp columns after a specified start...
Filter Tabular Dataset with time stamp columns before a specified end ...
Filter Tabular Dataset between a specified start and end time.
Filter Tabular Dataset to contain only the specified duration (amount)...
Generates the control script for the experiment.
Regenerate one of a web service's keys
Get the credentials for an AksCompute cluster
Return the best performing run amongst all completed runs
Get the hyperparameters for all child runs
Get the metrics from all child runs
Get all children for the current run selected by specified filters
Get the child runs sorted in descending order by best primary metric
Get an existing compute cluster
Get the context object for a run
Get Dataset by ID.
Get a registered Dataset from the workspace by its registration name.
Get an existing datastore
Get the default datastore for a workspace
Get the default keyvault for a workspace
Get an existing environment
Get a list of file paths for each file stream defined by the dataset.
Return the named list for input datasets.
Get a registered model
Get the Azure container registry that a packaged model uses
Get the model package creation logs
Get an experiment run
Get the details of a run
Get the details of a run along with the log files' contents
List the files that are stored in association with a run
Get the metrics logged to a run
Return a generator of the runs for an experiment
Get secrets from a keyvault
Get secrets from the keyvault associated with a run's workspace
Get a deployed web service
Retrieve auth keys for a web service
Retrieve the logs for a web service
Retrieve the auth token for a web service
Get an existing workspace
Get the details of a workspace
Specifies a Github package to install in environment
Define grid sampling over a hyperparameter search space
Create a configuration for a HyperDrive run
Create an inference configuration for model deployments
Install azureml sdk package
Manages authentication and acquires an authorization token in interact...
Call a web service with the provided input
Keep the specified columns and drops all others from the dataset.
Get the details (e.g IP address, port etc) of all the compute nodes in...
List the secrets in a keyvault
List the supported VM sizes in a region
List all workspaces that the user has access to in a subscription ID
Load all records from the dataset into a dataframe.
Load workspace configuration details from a config file
Create a deployment config for deploying a local web service
Log an accuracy table metric to a run
Log a confusion matrix metric to a run
Log an image metric to a run
Define a median stopping policy for early termination of HyperDrive ru...
Combine the results from the parallel training.
Create a context manager for mounting file streams defined by the data...
Specify a real value that is normally-distributed with mean mu and s...
Create a model package that packages all the assets needed to host a m...
Generate table of run details
Define supported metric goals for hyperparameter tuning
Defines options for how column headers are processed when reading data...
Pull the Docker image from a ModelPackage to your local Docker envir...
Specify a normal distribution of the form `round(exp(normal(mu, sigma)...
Specify a uniform distribution of the form `round(exp(uniform(min_valu...
Specify a normal distribution of the `form round(normal(mu, sigma) / q...
Specify a uniform distribution of the form `round(uniform(min_value, m...
Create an environment
Specify a set of random integers in the range [0, upper)
Define random sampling over a hyperparameter search space
Split file streams in the dataset into two parts randomly and approxim...
Register an Azure blob container as a datastore
Initialize a new Azure Data Lake Gen2 Datastore.
Register an Azure file share as a datastore
Initialize a new Azure PostgreSQL Datastore.
Initialize a new Azure SQL database Datastore.
Register a Dataset in the workspace
Registers AMLCompute as a parallel backend with the foreach package.
Register an environment in the workspace
Register a model to a given workspace
Register a model for operationalization.
Reload a local web service's entry script and dependencies
Initialize the ResourceConfiguration.
Save a Dockerfile and dependencies from a ModelPackage to your local...
Manages authentication using a service principle instead of a user ide...
Set the default datastore for a workspace
Add secrets to a keyvault
Skip file streams from the top of the dataset by the specified count.
Splits the job into parallel tasks.
Create an interactive logging run
Submit an experiment and return the active child run
Submit an experiment and return the active created run
Take a sample of file streams from top of the dataset by the specified...
Take a random sample of file streams in the dataset approximately by t...
Define a truncation selection policy for early termination of HyperDri...
Specify a uniform distribution of options to sample from
Unregister all versions under the registration name of this dataset fr...
Unregister a datastore from its associated workspace
Update a deployed ACI web service
Update a deployed AKS web service
Update scale settings for an AmlCompute cluster
Update a local web service
Upload files to the Azure storage a datastore points to
Upload files to a run
Upload a folder to a run
Upload a local directory to the Azure storage a datastore points to
Initialize run details widget
Wait for a web service to finish deploying
Wait for a model package to finish creating
Wait for a cluster to finish provisioning
Wait for the completion of a run
Write out the workspace configuration details to a config file
Interface to the 'Azure Machine Learning' Software Development Kit ('SDK'). Data scientists can use the 'SDK' to train, deploy, automate, and manage machine learning models on the 'Azure Machine Learning' service. To learn more about 'Azure Machine Learning' visit the website: <https://docs.microsoft.com/en-us/azure/machine-learning/service/overview-what-is-azure-ml>.
Useful links