Документация Майкрософт — новейшие статьи

Теперь техническая документация Майкрософт размещена на сайте docs.microsoft.com. Мы модернизировали не только веб-интерфейс, но и методы создания и поддержки материалов, которые помогают вам обучаться, развертывать решения и управлять ими. Это централизованное расположение для всего содержимого, связанного с технологиями Майкрософт. Чтобы вы не пропустили самые актуальные новости и интересные публикации на сайте docs.microsoft.com, мы предусмотрели для вас возможность выбрать требуемый веб-канал.


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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

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 : 21 октября 2019 г.

Azure NetAppFiles SDK Sample for .NET Core - Code Samples

https://docs.microsoft.com/en-us/samples/azure-samples/netappfiles-dotnetcore-sdk-sample/azure-netappfiles-sdk-sample-for-net-core/ This project demonstrates how to use a dotnet-core sample application to perform CRUD management operations for Microsoft.NetApp resource provider.
Published Date : 21 октября 2019 г.

Проверка сопоставления удостоверений для миграции SharePoint Active Directory

https://docs.microsoft.com/ru-ru/sharepointmigration/sharepoint-migration-identity-mapping-active-directory-identity-scan Предполагается, что пользователь с несопоставленным, так как он, скорее всего, не будет синк'ед в Azure Active Directory.
Published Date : 21 октября 2019 г.

Migration Manager settings

https://docs.microsoft.com/en-us/sharepointmigration/mo-settings A complete listing of the Migration Manager basic and advanced settings.
Published Date : 16 октября 2019 г.

Средство сопоставления удостоверений для миграции SharePoint Azure Active Directory — сканирование удостоверений

https://docs.microsoft.com/ru-ru/sharepointmigration/sharepoint-migration-identity-mapping-tool-azure-active-directory-identity-scan Целевым объектом является Онпремисессекуритидентифиер в Azure Active Directory. ... Для этого приложению необходимо разрешение на чтение Azure Active Directory.
Published Date : 21 октября 2019 г.

Средство сопоставления удостоверений для миграции в SharePoint

https://docs.microsoft.com/ru-ru/sharepointmigration/sharepoint-migration-identity-mapping-tool Используйте функцию сопоставления удостоверений средства оценки миграции SharePoint, чтобы помочь при миграции удостоверения.
Published Date : 21 октября 2019 г.

Заметки о выпусках Semi-Annual Channel (Targeted) в 2019 г.

https://docs.microsoft.com/ru-ru/officeupdates/semi-annual-channel-targeted-2019 Заметки о выпусках Semi-Annual Channel (Targeted) для Office 365 профессиональный плюс в 2019 г. для ИТ-специалистов
Published Date : 21 октября 2019 г.

Заметки о выпусках Monthly Channel в 2019 г.

https://docs.microsoft.com/ru-ru/officeupdates/monthly-channel-2019 Заметки о выпусках Monthly Channel для Office 365 профессиональный плюс в 2019 г. для ИТ-специалистов
Published Date : 21 октября 2019 г.

azureml.dataprep.api.builders module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders Builders module contains builder classes that could be used to create various transformation steps in interactive manner.
Published Date : 15 октября 2019 г.

Библиотеки мониторинга Azure для Python

https://docs.microsoft.com/ru-ru/python/api/overview/azure/monitoring/ Справочник по библиотекам мониторинга Azure для Python
Published Date : 21 октября 2019 г.

azureml.dataprep.DataProfile class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.dataprofile A DataProfile collects summary statistics on the data produced by a Dataflow.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedataset Models data associated with an input of a StepRun that comes from a . By default, the name of the dataset, the definition version, and the snapshot name (if snapshot is used) will be used as the name for the input. You can override the name with this class.
Published Date : 15 октября 2019 г.

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 : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts.event Event object for telemetry usage. Use events for collecting events with a defined schema.
Published Date : 30 сентября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.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 : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyper_drive_step.hyperdrivestep Creates an Azure ML Pipeline step to run hyperparameter tunning for Machine Learning model training. For an example of using HyperDriveStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-hyperdrive.
Published Date : 15 октября 2019 г.

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 : 15 октября 2019 г.

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 : 17 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricksstep Creates an Azure ML Pipeline step to add a DataBricks notebook, Python script, or JAR as a node. For an example of using DatabricksStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-databricks.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatchstep Creates an Azure ML Pipeline step for submitting jobs to Azure Batch. Note: This step does not support upload&#x2Fdownload of directories and their contents. For an example of using AzureBatchStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-azbatch.
Published Date : 15 октября 2019 г.

azureml.telemetry.contracts package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts Defines classes for collecting structured metrics and events telemetry. Structured telemetry is collected based on a common schema instead of free text logging of the data. Using a schema enables easier post-analysis of the data. Metrics and events in a common schema are collected with , , and .
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.automl_explain_utilities Utilities that could be used from AutoML after training for explaining AutoML models.
Published Date : 23 сентября 2019 г.

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 : 15 октября 2019 г.

azureml.pipeline.steps.databricks_step.DatabricksStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.databricks_step.databricksstep Creates an Azure ML Pipeline step to add a DataBricks notebook, Python script, or JAR as a node. For an example of using DatabricksStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-databricks.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.pipelinedataset Models data associated with an input of a StepRun that comes from a . By default, the name of the dataset, the definition version, and the snapshot name (if snapshot is used) will be used as the name for the input. You can override the name with this class.
Published Date : 15 октября 2019 г.

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 : 30 сентября 2019 г.

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 : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts.telemetryobjectbase Defines the base class for collecting schematized telemetry events and metrics. Use for collecting and aggregating data, and for collecting low volume events with a defined schema use events. Both types of telemetry use a schema instead of free text for logging the data. The schema defines required, standard, and extension fields.
Published Date : 30 сентября 2019 г.

azureml.pipeline.steps.module_step.ModuleStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.module_step.modulestep Creates an Azure ML pipeline step to run a specific version of a Module. objects define reusable computations, such as scripts or executables, that can be used in different machine learning scenarios and by different users. To use a specific version of a Module in a pipeline create a ModuleStep. A ModuleStep is a step in pipeline that uses an existing . For an example of using ModuleStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-modulestep.
Published Date : 15 октября 2019 г.

azureml.pipeline.steps.python_script_step.PythonScriptStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep Creates an Azure ML Pipeline step that runs Python script. For an example of using PythonScriptStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-get-started.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-telemetry/azureml.telemetry.contracts.requiredfields Defines Part A of the logging schema, keys that have a common meaning across telemetry data.
Published Date : 30 сентября 2019 г.

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 : 15 октября 2019 г.

azureml.pipeline.steps.azurebatch_step.AzureBatchStep class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.azurebatch_step.azurebatchstep Creates an Azure ML Pipeline step for submitting jobs to Azure Batch. Note: This step does not support upload&#x2Fdownload of directories and their contents. For an example of using AzureBatchStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-azbatch.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.modulestep Creates an Azure ML pipeline step to run a specific version of a Module. objects define reusable computations, such as scripts or executables, that can be used in different machine learning scenarios and by different users. To use a specific version of a Module in a pipeline create a ModuleStep. A ModuleStep is a step in pipeline that uses an existing . For an example of using ModuleStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-modulestep.
Published Date : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.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 : 15 октября 2019 г.

azureml.opendatasets.accessories package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories Accessory classes that help identify types of columns in data, e.g. lat&#x2Flong, zipcode, time, etc.
Published Date : 23 сентября 2019 г.

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 : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyperdrivestep Creates an Azure ML Pipeline step to run hyperparameter tunning for Machine Learning model training. For an example of using HyperDriveStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-hyperdrive.
Published Date : 15 октября 2019 г.

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 : 15 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.pythonscriptstep Creates an Azure ML Pipeline step that runs Python script. For an example of using PythonScriptStep, see the notebook https:&#x2F&#x2Faka.ms&#x2Fpl-get-started.
Published Date : 15 октября 2019 г.

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 : 15 октября 2019 г.

azureml.explain.model.scoring.scoring_explainer package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.scoring.scoring_explainer Init file for azureml-explain-model&#x2Fazureml&#x2Fexplain&#x2Fmodel&#x2Fscoring&#x2Fscoring_explainer.
Published Date : 15 октября 2019 г.

azureml.data.context_managers.DatastoreContextManager class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.context_managers.datastorecontextmanager Manage the context for datastore upload and download actions. This class is not intended to be used directly.
Published Date : 15 октября 2019 г.

azureml.dataprep package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep Main Data Prep module that contains tools to load, analyze and manipulate data. To learn more about the advantages, key functionalities and supported platforms of Data Prep, you may refer to https:&#x2F&#x2Faka.ms&#x2Fdata-prep-sdk.
Published Date : 17 октября 2019 г.

azureml.core.dataset.Dataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.dataset.dataset Represents a resource for exploring, transforming, and managing data in Azure Machine Learning. A Dataset is a reference to data in a . The following Datasets types are supported: represents data in a tabular format created by parsing the provided file or list of files. references single or multiple files in datastores or from public URLs. You can explore data in a Dataset with summary statistics and transform it using intelligent transforms. When you are ready to use the data for training, you can save the Dataset to your Azure Machine Learning workspace as a versioned Dataset. To get started with datasets, see the article Add &ampamp; register datasets, or see the notebooks https:&#x2F&#x2Faka.ms&#x2Ftabulardataset-samplenotebook and https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : 15 октября 2019 г.

azureml.data.dataset_definition.DatasetDefinition class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_definition.datasetdefinition Defines a series of steps that specify how to read and transform data in a Dataset. A Dataset registered in an Azure Machine Learning workspace can have multiple definitions, each created by calling . Each definition has an unique identifier. The current definition is the latest one created. For unregistered Datasets, only one definition exists. Dataset definitions support all the transformations listed for the class: see http:&#x2F&#x2Faka.ms&#x2Fazureml&#x2Fhowto&#x2Ftransformdata. To learn more about Dataset Definitions, go to https:&#x2F&#x2Faka.ms&#x2Fazureml&#x2Fhowto&#x2Fversiondata.
Published Date : 15 октября 2019 г.

azureml.core.Dataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.dataset(class) Represents a resource for exploring, transforming, and managing data in Azure Machine Learning. A Dataset is a reference to data in a . The following Datasets types are supported: represents data in a tabular format created by parsing the provided file or list of files. references single or multiple files in datastores or from public URLs. You can explore data in a Dataset with summary statistics and transform it using intelligent transforms. When you are ready to use the data for training, you can save the Dataset to your Azure Machine Learning workspace as a versioned Dataset. To get started with datasets, see the article Add &ampamp; register datasets, or see the notebooks https:&#x2F&#x2Faka.ms&#x2Ftabulardataset-samplenotebook and https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : 15 октября 2019 г.

azureml.data.context_managers module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.context_managers Contains functionality to manage data context of datastores and datasets. Internal use only.
Published Date : 15 октября 2019 г.

azureml.dataprep.ParseDelimitedProperties class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.parsedelimitedproperties Describes and stores the properties required to parse a Delimited Text-file.
Published Date : 15 октября 2019 г.

azureml.data.dataset_definition module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_definition Contains functionality to manage dataset definition and its operations.
Published Date : 15 октября 2019 г.

Квоты и ограничения платформы

https://docs.microsoft.com/ru-ru/azure/media-services/latest/limits-quotas-constraints В этом разделе описываются квоты и ограничения платформы &quotСлужбы мультимедиа Azure&quot версии 3.
Published Date : 21 октября 2019 г.

azureml.opendatasets.enrichers package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.enrichers An enricher is a class responsible for enrich customer data with open data. But essentially they can be any data that make sense to be joined together.
Published Date : 17 октября 2019 г.

azureml.opendatasets.aggregators package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.aggregators An aggregator defines the aggregations needed after the join. If no aggregation is needed, use aggregator_all.
Published Date : 17 октября 2019 г.

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

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.joinbuilder An interactive object that can be used to help join two Dataflows. Note This builder has the ability to detect and suggest potential join arguments. In some cases, the builder can derive the join key in one of the Dataflows and use the derived key column to perform a join.
Published Date : 17 октября 2019 г.

Getting started with the App Center Auth Service - Visual Studio App Center

https://docs.microsoft.com/en-us/appcenter/auth/getting-started How to get started with the App Center Auth Service
Published Date : 21 октября 2019 г.

Choose the right service for app builds - Visual Studio App Center

https://docs.microsoft.com/en-us/appcenter/build/choose-between-services Helps user choose between Visual Studio AppCenter and Azure DevOps Pipelines for Building their mobile Apps.
Published Date : 21 октября 2019 г.

Managing source repository connections - Visual Studio App Center

https://docs.microsoft.com/en-us/appcenter/build/connect How to connect&#x2Freconnect to a repository to start building your app
Published Date : 21 октября 2019 г.

Contoso Airlines Flight Team Provisioning Sample - Code Samples

https://docs.microsoft.com/en-us/samples/microsoftgraph/contoso-airlines-azure-functions-sample/contoso-airlines-flight-team-provisioning-sample/ This sample app implements Azure functions designed to be invoked via a Graph webhook to provision a Microsoft Team when a new flight is added to a ma
Published Date : 21 октября 2019 г.

Virtual datacenters: A network perspective

https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/reference/networking-vdc Learn how to build a virtual datacenter in Azure.
Published Date : 21 октября 2019 г.