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.
Knowledge Mining Solution Accelerator Guide - Code Samples
https://docs.microsoft.com/en-us/samples/azure-samples/azure-search-knowledge-mining/azure-search-knowledge-mining/ Welcome to the Knowledge Mining Solution Accelerator!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 innovation guide: Shared solutions and feedback loops - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/adoption Azure innovation guide - Shared solutions and feedback loopsPublished Date : 21 октября 2019 г.
Azure innovation guide: Predict and influence - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/predict Azure innovation guide - Predict and influencePublished Date : 21 октября 2019 г.
Azure innovation guide: Continuous adoption - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/ci-cd Learn to innovate with continuous adoption using Azure.Published Date : 21 октября 2019 г.
Azure management guide: Before you start - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/manage/azure-management-guide/ Learn how to manage your Azure operations with step-by-step guidance.Published Date : 21 октября 2019 г.
Azure innovation guide: Before you start - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/ Learn to create innovative new solutions for your customers using Azure.Published Date : 21 октября 2019 г.
Azure innovation guide: Interact through devices - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/devices Azure innovation guide - Interact through devicesPublished Date : 21 октября 2019 г.
Azure innovation guide: Engage through apps - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/innovate/innovation-guide/apps Learn to innovate by engaging through apps using Azure.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.
Обзор средства оценки миграции SharePoint
https://docs.microsoft.com/ru-ru/sharepointmigration/overview-of-the-sharepoint-migration-assessment-tool Обзор средства оценки миграции SharePointPublished 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-специалистовPublished 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-специалистовPublished Date : 21 октября 2019 г.
azureml.dataprep.api.builders.LabelEncoderBuilder class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.labelencoderbuilder Interactive object that can be used to generate encoded labels.Published Date : 15 октября 2019 г.
API Reference - Azure Machine Learning Data Prep Python
https://docs.microsoft.com/ru-ru/python/api/overview/azure/dataprep/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.
azureml.dataprep.api.builders.Builders class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.builders Exposes all available builders for a given Dataflow.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 PythonPublished 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:/˽ocs.microsoft.com˾n-us˺zure/machine-learning/service/how-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 = 'regression', X = your_training_features, y = your_training_labels, iterations=30, iteration_timeout_minutes=5, primary_metric="spearman_correlation") ws = Workspace.from_config() experiment = Experiment(ws, "your-experiment-name") run = experiment.submit(automated_ml_config, show_output=True)Published Date : 15 октября 2019 г.
API Reference - Azure Machine Learning Python
https://docs.microsoft.com/ru-ru/python/api/overview/azure/ml/Published Date : 21 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:/˺ka.ms/pl-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:/˺ka.ms/pl-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:/˺ka.ms/pl-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's inputs and outputs to the object's 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:/˺ka.ms/pl-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˽ownload of directories and their contents. For an example of using AzureBatchStep, see the notebook https:/˺ka.ms/pl-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:/˺ka.ms/pl-style-trans.Published Date : 15 октября 2019 г.
azureml.pipeline.steps.r_script_step.RScriptStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.r_script_step.rscriptstep Creates an Azure ML Pipeline step that runs R script.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:/˺ka.ms/pl-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:/˺ka.ms/pl-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:/˺ka.ms/pl-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:/˺ka.ms/pl-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˽ownload of directories and their contents. For an example of using AzureBatchStep, see the notebook https:/˺ka.ms/pl-azbatch.Published Date : 15 октября 2019 г.
azureml.pipeline.steps.RScriptStep class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-steps/azureml.pipeline.steps.rscriptstep Creates an Azure ML Pipeline step that runs R script.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:/˺ka.ms/pl-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:/˺ka.ms/pl-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/long, 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:/˺ka.ms/pl-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:/˺ka.ms/pl-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:/˺ka.ms/pl-data-trans.Published Date : 15 октября 2019 г.
azureml.explain.model.scoring.scoring_explainer.KernelScoringExplainer class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.scoring.scoring_explainer.kernelscoringexplainer Defines a scoring model based on KernelExplainer.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:/˺ka.ms/pl-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:/˺ka.ms/pl-adla.Published Date : 15 октября 2019 г.
azureml.opendatasets.accessories.nyc_tlc_yellow_accessory module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.nyc_tlc_yellow_accessory NYC Taxi yellow trip data.Published Date : 17 октября 2019 г.
azureml.opendatasets.accessories.us_labor_ppi_industry_accessory module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.us_labor_ppi_industry_accessory US labor ppi industry accessory.Published Date : 23 сентября 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˺zureml˾xplain/model/scoring/scoring_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:/˺ka.ms⿚ta-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 & register datasets, or see the notebooks https:/˺ka.ms/tabulardataset-samplenotebook and https:/˺ka.ms˿iledataset-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:/˺ka.ms˺zureml/howto/transformdata. To learn more about Dataset Definitions, go to https:/˺ka.ms˺zureml/howto/versiondata.Published Date : 15 октября 2019 г.
azureml.dataprep.STypeCountEntry class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.stypecountentryPublished 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 & register datasets, or see the notebooks https:/˺ka.ms/tabulardataset-samplenotebook and https:/˺ka.ms˿iledataset-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 "Службы мультимедиа Azure" версии 3.Published Date : 21 октября 2019 г.
azureml.train.automl.exceptions module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions The set of custom exceptions produced by AutoMl package.Published Date : 17 октября 2019 г.
azureml.opendatasets.accessories.country_region_data.CountryOrRegionData class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.country_region_data.countryorregiondata A class defines location two related properties below. countrycode_column_name country_or_region_column_namePublished Date : 17 октября 2019 г.
azureml.train.automl.exceptions.DataScriptException class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-train-automl/azureml.train.automl.exceptions.datascriptexception Exception for issues with user's get_data script.Published Date : 30 сентября 2019 г.
azureml.opendatasets.enrichers.common_weather_enricher module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.enrichers.common_weather_enricher Common weather enricher class.Published Date : 17 октября 2019 г.
azureml.opendatasets.accessories.country_or_region_time_customer_data.CountryOrRegionTimeCustomerData class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.country_or_region_time_customer_data.countryorregiontimecustomerdata A customer_data class which contains location column and time column.Published Date : 17 октября 2019 г.
azureml.opendatasets.aggregators.aggregator_avg module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.aggregators.aggregator_avg Aggregator avg class.Published Date : 17 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.accessories.public_holidays_offline_accessory.PublicHolidaysOfflineAccessory class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.public_holidays_offline_accessory.publicholidaysofflineaccessory Public holiday class.Published Date : 17 октября 2019 г.
azureml.opendatasets.accessories.country_or_region_time_public_data.CountryOrRegionTimePublicData class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.country_or_region_time_public_data.countryorregiontimepublicdata A class wrapper public_data which contains both country or region column and time column.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.opendatasets.accessories.public_holidays_accessory.PublicHolidaysAccessory class - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.public_holidays_accessory.publicholidaysaccessory Public holiday accessory class.Published Date : 15 октября 2019 г.
azureml.opendatasets.accessories.country_region_data module - Azure Machine Learning Python
https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.accessories.country_region_data Location data, with supported column classes.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 ServicePublished 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/reconnect to a repository to start building your appPublished 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 maPublished Date : 21 октября 2019 г.
Introduction to the operating model - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/operating-model/ Understand the operating model of the Cloud Adoption Framework.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.
Operating model terminology - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/operating-model/terms Understand the terminology related to an operating model.Published Date : 21 октября 2019 г.
Protect and recover in Azure - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/manage/azure-management-guide/protect-recover Ensure business stability by decreasing recovery timePublished Date : 21 октября 2019 г.
Cloud Management and Operations - Guiding theory - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/manage/considerations/ Cloud Management and Operations - Guiding theory.Published Date : 21 октября 2019 г.
Platform specialization for cloud management in Azure - Microsoft Cloud Adoption Framework for Azure
https://docs.microsoft.com/en-us/azure/cloud-adoption-framework/manage/azure-management-guide/platform-specialization Improve platform-specific cloud management operations.Published Date : 21 октября 2019 г.