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Selected Feed: Azure

Prepare your environment for Surface Hub 2S

https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-prepare-environment Learn what you need to do to prepare your environment for Surface Hub 2S.
Published Date : 2019年10月21日

Manage Surface Hub 2S with Intune

https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-manage-intune Learn how to update and manage Surface Hub 2S using Intune.
Published Date : 2019年10月21日

First time Setup for Surface Hub 2S

https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-setup Learn how to complete first time Setup for Surface Hub 2S.
Published Date : 2019年10月21日

Surface Hub 2S deployment checklists

https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-deploy-checklist Verify your deployment of Surface Hub 2S using pre- and post-deployment checklists.
Published Date : 2019年10月21日

Create provisioning packages for Surface Hub 2S

https://docs.microsoft.com/en-us/surface-hub/surface-hub-2s-deploy This page describes how to deploy Surface Hub 2S using provisioning packages and other tools.
Published Date : 2019年10月21日

Controlling access to Common Data Service - Power Platform Admin center

https://docs.microsoft.com/en-us/power-platform/admin/wp-controlling-access Provides information about how you can control access to Common Data Service using Azure AD.
Published Date : 2019年10月21日

Test recorder and Regression suite automation tool for Retail Cloud POS - Retail | Dynamics 365

https://docs.microsoft.com/en-us/dynamics365/retail/dev-itpro/pos-rsat This topic explains how to automate user acceptance testing (UAT) by using the POS test recorder and the Regression suite automation tool (RSAT).
Published Date : 2019年10月21日

Anomaly Detector API Samples - Code Samples

https://docs.microsoft.com/en-us/samples/azure-samples/anomalydetector/anomalydetector/ This repository contains samples for Anomaly Detector API. The Anomaly Detector API enables you to monitor and find abnormalities in your time series data by automatically identifying and applying the correct statistical models, regardless of industry, scenario, or data volume.
Published Date : 2019年10月21日

azure.batch.BatchServiceClient class

https://docs.microsoft.com/en-us/python/api/azure.batch.batchserviceclient A client for issuing REST requests to the Azure Batch service.
Published Date : 2019年10月15日

azureml.dataprep.FileFormatArguments class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.fileformatarguments Defines and stores the arguments which can affect learning on a &#x27FileFormatBuilder&#x27.
Published Date : 2019年10月15日

azureml.dataprep.ParseLinesProperties class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.parselinesproperties Describes and stores the properties required to parse a Text-file containing raw lines.
Published Date : 2019年10月15日

azureml.dataprep.api.builders.PivotBuilder class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.pivotbuilder Interactive object that can be used to generate pivoted columns from the selected pivot columns.
Published Date : 2019年10月15日

用于 Python 的 Azure 监视库

https://docs.microsoft.com/zh-cn/python/api/overview/azure/monitoring/ 用于 Python 的 Azure 监视库参考
Published Date : 2019年10月21日

azureml.pipeline.steps.adla_step.AdlaStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adla_step.adlastep Creates an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics. For an example of using this AdlaStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-adla.
Published Date : 2019年10月15日

azureml.pipeline.core.module.Module class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.module.module Represents a computation unit used in a Azure Machine Learning pipeline. A module is a collection of files which will run on a compute target and a description of an interface. The collection of files can be script, binaries, or any other files required to execute on the compute target. The module interface describes inputs, outputs, and parameter definitions. It doesn&#x27t bind them to specific values or data. A module has a snapshot associated with it, which captures the collection of files defined for the module.
Published Date : 2019年10月15日

azureml.train.hyperdrive.BanditPolicy class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.banditpolicy Defines an early termination policy based on slack criteria, and a frequency and delay interval for evaluation.
Published Date : 2019年9月23日

azureml.pipeline.core.run.PipelineRun class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.run.pipelinerun Represents a run of a . This class can be used to manage, check status, and retrieve run details once a pipeline run is submitted. Use to retrieve the objects which are created by the pipeline run. Other uses include retrieving the object associated with the pipeline run, fetching the status of the pipeline run, and waiting for run completion.
Published Date : 2019年10月15日

azureml.train.hyperdrive.PrimaryMetricGoal enum - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.primarymetricgoal Defines supported metric goals for hyperparameter tuning. A metric goal is used to determine whether a higher value for a metric is better or worse. Metric goals are used when comparing runs based on the primary metric. For example, you may want to maximize accuracy or minimize error. The primary metric name and goal are specified in the class when you configure a HyperDrive run.
Published Date : 2019年9月30日

azureml.train.automl.run module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.run The automated machine learning Run class. Provides methods for starting&#x2Fstopping runs, monitoring run status, and retrieving model output.
Published Date : 2019年10月15日

azureml.train.hyperdrive.HyperParameterSampling class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.hyperparametersampling Abstract base class for all hyperparameter sampling algorithms. This class encapsulates the hyperparameter space, the sampling method, and additional properties for derived sampling classes: , , and .
Published Date : 2019年9月23日

azureml.train.hyperdrive.NoTerminationPolicy class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.hyperdrive.noterminationpolicy Specifies that no early termination policy is applied. Each run will execute until completion.
Published Date : 2019年9月23日

azureml.train.sklearn.SKLearn class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.sklearn.sklearn Creates an estimator for training in Scikit-learn experiments. This estimator only supports single-node CPU training. Supported versions: 0.20.3
Published Date : 2019年9月30日

azureml.pipeline.steps.data_transfer_step module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step Contains functionality to create an Azure ML Pipeline step that transfers data between storage options.
Published Date : 2019年10月15日

azureml.pipeline.steps.estimator_step.EstimatorStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step.estimatorstep Creates an Azure ML Pipeline step to run Estimator for Machine Learning model training. For an example of using EstimatorStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-estimator.
Published Date : 2019年10月15日

azureml.pipeline.steps.DataTransferStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.datatransferstep Creates an Azure ML Pipeline step that transfers data between storage options. This step supports the following storage types as sources and sinks except where noted: Azure Blob Storage Azure Data Lake Storage Gen1 and Gen2 Azure SQL Database Azure Database for PostgreSQL (source only) For an example of using DataTransferStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-data-trans.
Published Date : 2019年10月15日

azureml.telemetry.activity.ActivityType class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.activity.activitytype The type of activity (code) monitored. The default type is &ampquot;PublicAPI&ampquot;.
Published Date : 2019年10月15日

azureml.train.automl.AutoMLConfig class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automlconfig Configuration for submitting an Automated Machine Learning experiment in Azure Machine Learning service. This configuration object contains and persists the parameters for configuring the experiment run parameters, as well as the training data to be used at run time. For guidance on selecting your settings, you may refer to https:&#x2F&#x2Fdocs.microsoft.com&#x2Fen-us&#x2Fazure&#x2Fmachine-learning&#x2Fservice&#x2Fhow-to-configure-auto-train. The following code shows a basic example of creating an AutoMLConfig object, and submitting an experiment with the defined configuration: from azureml.core.experiment import Experiment from azureml.core.workspace import Workspace from azureml.train.automl import AutoMLConfig automated_ml_config = AutoMLConfig(task = &#x27regression&#x27, X = your_training_features, y = your_training_labels, iterations=30, iteration_timeout_minutes=5, primary_metric=&ampquot;spearman_correlation&ampquot;) ws = Workspace.from_config() experiment = Experiment(ws, &ampquot;your-experiment-name&ampquot;) run = experiment.submit(automated_ml_config, show_output=True)
Published Date : 2019年10月15日

azureml.pipeline.steps.mpi_step.MpiStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step.mpistep Creates an Azure ML pipeline step to run an MPI job. For an example of using MpiStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-style-trans.
Published Date : 2019年10月15日

azureml.telemetry package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry Provides functionality for logging, capturing events and metrics, and monitoring code activity. This package enables you to collect different types of telemetry using free text or structured logging. For example, for unstructured text in high volumes you can use one of the logs from the module. For collecting and aggregating metrics or capturing low volume events or user activities with a defined schema use the structured schema defined in the module. You can also, monitor blocks of code with the the module. Log messages, metrics, events, and activity messages can written to Application Insights. For example, you can the function to get a handle to an Application Insights instance.
Published Date : 2019年9月30日

azureml.pipeline.steps.hyper_drive_step.HyperDriveStepRun class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivesteprun Manage, check status, and retrieve run details for a pipeline step. HyperDriveStepRun provides the functionality of with the additional support of . The HyperDriveStepRun class enables you to manage, check status, and retrieve run details for the HyperDrive run and each of its generated child runs. The StepRun class enables you to do this once the parent pipeline run is submitted and the pipeline has submitted the step run.
Published Date : 2019年9月30日

azureml.pipeline.steps.AdlaStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.adlastep Creates an Azure ML Pipeline step to run a U-SQL script with Azure Data Lake Analytics. For an example of using this AdlaStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-adla.
Published Date : 2019年10月15日

azureml.telemetry.contracts.Metric class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts.metric Metric object for telemetry usage. Use metrics for collecting and aggregating data that can be best aggregated into buckets for analysis.
Published Date : 2019年10月15日

azureml.pipeline.steps.data_transfer_step.DataTransferStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.data_transfer_step.datatransferstep Creates an Azure ML Pipeline step that transfers data between storage options. This step supports the following storage types as sources and sinks except where noted: Azure Blob Storage Azure Data Lake Storage Gen1 and Gen2 Azure SQL Database Azure Database for PostgreSQL (source only) For an example of using DataTransferStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-data-trans.
Published Date : 2019年10月15日

azureml.pipeline.core.module_step_base.ModuleStepBase class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.module_step_base.modulestepbase Adds a step to a pipeline that uses a specific module. A derives from ModuleStepBase and is a node in a pipeline that uses an existing , and specifically, one of its versions. In order to define which ModuleVersion would eventually be used in the submitted pipeline, you can define one of the following when creating the ModuleStep: object object and a version value Only Module without a version value; in this case, the version resolution used may vary across submissions. You also need to define the mapping between the step&#x27s inputs and outputs to the object&#x27s inputs and outputs.
Published Date : 2019年10月17日

azureml.pipeline.steps.databricks_step module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step Contains functionality to create an Azure ML pipeline step to run a Databricks notebook or Python script on DBFS.
Published Date : 2019年10月15日

azureml.pipeline.steps.mpi_step module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.mpi_step Contains functionality to add a Azure ML Pipeline step to run an MPI job for Machine Learning model training.
Published Date : 2019年10月15日

azureml.dataprep.Dataflow class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.dataflow A Dataflow represents a series of lazily-evaluated, immutable operations on data. It is only an execution plan. No data is loaded from the source until you get data from the Dataflow using one of head, to_pandas_dataframe, get_profile or the write methods.
Published Date : 2019年10月15日

azureml.train.automl.ensemble.Ensemble class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.ensemble.ensemble Class for ensembling previous AutoML iterations. The ensemble pipeline is initialized from a collection of already fitted pipelines. :param automl_settings -- The settings for this current experiment :param ensemble_run_id -- The id of the current ensembling run :param experiment_name -- The name of the current Azure ML experiment :param workspace_name -- The name of the current Azure ML workspace where the experiment is run :param subscription_id -- The id of the current Azure ML subscription where the experiment is run :param resource_group_name -- The name of the current Azure resource group
Published Date : 2019年10月17日

azureml.pipeline.steps.estimator_step module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimator_step Contains functionality to create a pipeline step that runs an Estimator for Machine Learning model training.
Published Date : 2019年10月15日

azureml.pipeline.core.StepRun class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.steprun A run of a step in a . This class can be used to manage, check status, and retrieve run details once the parent pipeline run is submitted and the pipeline has submitted the step run.
Published Date : 2019年10月15日

azureml.pipeline.core.builder.PipelineStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinestep Represents an execution step in an Azure Machine Learning pipeline. Pipelines are constructed from multiple pipeline steps, which are distinct computational units in the pipeline. Each step can run independently and use isolated compute resources. Each step typically has its own named inputs, outputs, and parameters. The PipelineStep class is the base class from which other built-in step classes designed for common scenarios inherit, such as , , and . For an overview of how Pipelines and PipelineSteps relate, see What are ML Pipelines.
Published Date : 2019年10月15日

azureml.pipeline.core.PipelineRun class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinerun Represents a run of a . This class can be used to manage, check status, and retrieve run details once a pipeline run is submitted. Use to retrieve the objects which are created by the pipeline run. Other uses include retrieving the object associated with the pipeline run, fetching the status of the pipeline run, and waiting for run completion.
Published Date : 2019年10月15日

azureml.pipeline.core.builder.PipelineData class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.builder.pipelinedata Represents intermediate data in an Azure Machine Learning pipeline. Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Published Date : 2019年10月15日

azureml.pipeline.core.PipelineData class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata Represents intermediate data in an Azure Machine Learning pipeline. Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
Published Date : 2019年10月15日

azureml.pipeline.core.graph.OutputPortBinding class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.outputportbinding OutputPortBinding specifies a named output of a step. OutputPortBinding can be used to specify the type of data which will be produced by a step and how the data will be produced. It can be used with to specify that the step output is a required input of another step.
Published Date : 2019年10月15日

azureml.pipeline.core.PipelineStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinestep Represents an execution step in an Azure Machine Learning pipeline. Pipelines are constructed from multiple pipeline steps, which are distinct computational units in the pipeline. Each step can run independently and use isolated compute resources. Each step typically has its own named inputs, outputs, and parameters. The PipelineStep class is the base class from which other built-in step classes designed for common scenarios inherit, such as , , and . For an overview of how Pipelines and PipelineSteps relate, see What are ML Pipelines.
Published Date : 2019年10月15日

azureml.opendatasets package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets Enable consuming Azure open datasets into dataframes and enrich customer data.
Published Date : 2019年10月15日

azureml.pipeline.core.graph.InputPortBinding class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.inputportbinding Defines a binding from a source to an input of a step. An InputPortBinding can be used as an input to a step. The source can be a , , , , or . InputPortBinding is useful to specify the name of the step input, if it should be different than the name of the bind object (i.e. to avoid duplicate input&#x2Foutput names or because the step script needs an input to have a certain name). It can also be used to specify the bind_mode for inputs.
Published Date : 2019年10月15日