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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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

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 : Montag, 21. Oktober 2019

Cloud Core Services: Verwalten von Diensten mit dem Azure-Portal - Learn

https://docs.microsoft.com/de-de/learn/modules/tour-azure-portal/ Hier finden Sie eine Übersicht über die Features und Dienste des Azure-Portals und erfahren, wie Sie das Portal anpassen können.
Published Date : Montag, 21. Oktober 2019

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 : Dienstag, 15. Oktober 2019

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 : Donnerstag, 17. Oktober 2019

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 : Dienstag, 15. Oktober 2019

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

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.builders.columntypesbuilder Interactive object that can be used to infer column types and type conversion attributes.
Published Date : Montag, 30. September 2019

Azure-Überwachungsbibliotheken für Python

https://docs.microsoft.com/de-de/python/api/overview/azure/monitoring/ Referenz zu Azure-Überwachungsbibliotheken für Python
Published Date : Montag, 21. Oktober 2019

azureml.train.estimator.Estimator class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.estimator.estimator Represents a generic estimator to train data using any supplied framework. This class is designed for use with machine learning frameworks that do not already have an Azure Machine Learning pre-configured estimator. Pre-configured estimators exist for , , , and . The Estimator class wraps run configuration information to help simplify the tasks of specifying how a script is executed. It supports single-node as well as multi-node execution. Running the estimator produces a model in the output directory specified in your training script.
Published Date : Donnerstag, 17. Oktober 2019

azureml.train.dnn.TensorFlow class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.tensorflow Represents an estimator for training in TensorFlow experiments. Supported versions: 1.10, 1.12, 1.13, 2.0
Published Date : Dienstag, 15. Oktober 2019

azureml.train.dnn.PyTorch class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.pytorch Represents an estimator for training in PyTorch experiments. Supported versions: 1.0, 1.1, 1.2
Published Date : Montag, 30. September 2019

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 : Montag, 30. September 2019

azureml.train.dnn.Chainer class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-train-core/azureml.train.dnn.chainer Represents an estimator for training in Chainer experiments. Supported versions: 5.1.0
Published Date : Montag, 30. September 2019

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.outputport Instance of an output port on a node, which can be connected to an input port.
Published Date : Dienstag, 15. Oktober 2019

azureml.opendatasets.selectors.enricher_selector module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.selectors.enricher_selector EnricherSelector is the root class of LocationClosestSelector and TimeNearestSelector. Two subclass: EnricherLocationSelector: provides some basic calculations of spherical distance EnricherTimeSelector: provides round_to wrapper functions
Published Date : Donnerstag, 17. Oktober 2019

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 : Dienstag, 15. Oktober 2019

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 : Dienstag, 15. Oktober 2019

azureml.pipeline.core.graph module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph Defines classes for constructing Azure Machine Learning pipeline graphs. Azure ML pipeline graphs are created for objects, when you use (and derived classes), , and objects. In typical use cases, you will not need to directly use the classes in this module. A pipeline run graph consists of module nodes which represent basic units such as a datasource or step. Nodes can have input ports and output ports, and associated parameters. Edges define relationships between two node ports in a graph.
Published Date : Dienstag, 15. Oktober 2019

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

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.graph.node Represents a basic unit in a graph, e.g it could be any datasource or step.
Published Date : Donnerstag, 17. Oktober 2019

azureml.opendatasets.selectors package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-opendatasets/azureml.opendatasets.selectors Selectors define logics to select columns from both customer and public data to join together. Examples: join by finding the nearest X locations, or by rounding to the same time granularity.
Published Date : Donnerstag, 17. Oktober 2019

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 : Montag, 23. September 2019

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 : Dienstag, 15. Oktober 2019

azureml.explain.model.common.model_wrapper package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.common.model_wrapper Init file for azureml-explain-model&#x2Fazureml&#x2Fexplain&#x2Fmodel&#x2Fcommon&#x2Fmodel_wrapper.
Published Date : Dienstag, 15. Oktober 2019

azureml.explain.model.explanation.explanation package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.explanation.explanation Init file for azureml-explain-model&#x2Fazureml&#x2Fexplain&#x2Fmodel&#x2Fexplanation.
Published Date : Dienstag, 15. Oktober 2019

azureml.explain.model package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model Module for model interpretability, including feature and class importance for blackbox and whitebox models. You can use model interpretability to explain why a model model makes the predictions it does and help build confidence in the model. For more information, see the article https:&#x2F&#x2Fdocs.microsoft.com&#x2Fen-us&#x2Fazure&#x2Fmachine-learning&#x2Fservice&#x2Fmachine-learning-interpretability-explainability.
Published Date : Dienstag, 15. Oktober 2019

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 : Dienstag, 15. Oktober 2019

azureml.data.TabularDataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset Represents a tabular dataset to use in Azure Machine Learning service. A TabularDataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation. Data is not loaded from the source until TabularDataset is asked to deliver data. TabularDataset is created using methods like from the class. For more information, see the article Add &ampamp; register datasets. To get started working with a tabular dataset, see https:&#x2F&#x2Faka.ms&#x2Ftabulardataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

azureml.explain.model.common package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.common Common infrastructure, class hierarchy and utilities for model explanations.
Published Date : Dienstag, 15. Oktober 2019

azureml.dataprep.api.functions.RegEx class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-dataprep/azureml.dataprep.api.functions.regex The RegEx class makes it possible to create expressions that leverage regular expressions.
Published Date : Dienstag, 15. Oktober 2019

azureml.data package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data Contains modules supporting data representation for Datastore and Dataset in Azure Machine Learning. This package contains core functionality supporting and classes in the package. Datastore objects contain connection information to Azure storage services that can be easily referred to by name without the need to work directly with or hard code connection information in scripts. Datastore supports a number of different services represented by classes in this package, including , , and . For a full list of supported storage services, see the class. While a Datastore acts as a container for your data files, you can think of a Dataset as a reference or pointer to specific data that&#x27s in your datastore. 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 your datastores or public URLs. For more information, see the article Add &ampamp; register datasets. To get started working with a datasets, see https:&#x2F&#x2Faka.ms&#x2Ftabulardataset-samplenotebook and https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

azureml.explain.model.common.constants package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.common.constants Init file for azureml-explain-model&#x2Fazureml&#x2Fexplain&#x2Fmodel&#x2Fconstants.
Published Date : Dienstag, 15. Oktober 2019

azureml.explain.model.common.base_explainer package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-explain-model/azureml.explain.model.common.base_explainer Init file for azureml-explain-model&#x2Fazureml&#x2Fexplain&#x2Fmodel&#x2Fcommon&#x2Fbase_explainer.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.dataset_action_run.DatasetActionRun class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_action_run.datasetactionrun Manage the execution of Dataset actions. DatasetActionRun provides methods for monitoring the status of long running actions on datasets. It also provides a method to get the result of an action after completion.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.file_dataset.FileDataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.file_dataset.filedataset Represents a collection of file references in datastores or public URLs to use in Azure Machine Learning. A FileDataset defines a series of lazily-evaluated, immutable operations to load data from the data source into file streams. Data is not loaded from the source until FileDataset is asked to deliver data. A FileDataset is created using the method of the FileDatasetFactory class. For more information, see the article Add &ampamp; register datasets. To get started working with a file dataset, see https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.tabular_dataset.TabularDataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabular_dataset.tabulardataset Represents a tabular dataset to use in Azure Machine Learning service. A TabularDataset defines a series of lazily-evaluated, immutable operations to load data from the data source into tabular representation. Data is not loaded from the source until TabularDataset is asked to deliver data. TabularDataset is created using methods like from the class. For more information, see the article Add &ampamp; register datasets. To get started working with a tabular dataset, see https:&#x2F&#x2Faka.ms&#x2Ftabulardataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.dataset_snapshot.DatasetSnapshot class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_snapshot.datasetsnapshot Manages Dataset snapshots with operations to get a snapsot, return its status, and convert it to a dataframe. A DataSnapshot object is returned from the method of the class.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.dataset_factory.FileDatasetFactory class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.filedatasetfactory Contains methods to create a file dataset for Azure Machine Learning service. A is created from the method defined in this class. For more information on working with file datasets, see the notebook https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

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 : Dienstag, 15. Oktober 2019

azureml.data.dataset_snapshot module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_snapshot Contains functionality to manage Dataset snapshot operations.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.dataset_factory module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory Contains functionality to create datasets for Azure Machine Learning service.
Published Date : Dienstag, 15. Oktober 2019

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 : Dienstag, 15. Oktober 2019

azureml.data.dataset_action_run module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_action_run Contains functionality that manages the execution of Dataset actions. This module provides convenience methods for creating Dataset actions and get their results after completion.
Published Date : Dienstag, 15. Oktober 2019

azureml.core package - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core Contains core packages, modules and classes for Azure Machine Learning. Main areas include managing compute targets, creating&#x2Fmanaging workspaces and experiments, and submitting&#x2Faccessing model runs and run output&#x2Flogging.
Published Date : Dienstag, 15. Oktober 2019

azureml.data.FileDataset class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.data.filedataset Represents a collection of file references in datastores or public URLs to use in Azure Machine Learning. A FileDataset defines a series of lazily-evaluated, immutable operations to load data from the data source into file streams. Data is not loaded from the source until FileDataset is asked to deliver data. A FileDataset is created using the method of the FileDatasetFactory class. For more information, see the article Add &ampamp; register datasets. To get started working with a file dataset, see https:&#x2F&#x2Faka.ms&#x2Ffiledataset-samplenotebook.
Published Date : Dienstag, 15. Oktober 2019

azureml.core.compute_target module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute_target Classes for managing various compute target options. Compute targets define your training compute environment, and can be either local, or remote resources in the cloud. Remote resources allow you to easily scale up or scale out your machine learning experimentation by taking advantage of accelerated CPU and GPU processing capabilities.
Published Date : Dienstag, 15. Oktober 2019

azureml.accel.models.accel_model.AccelModel class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models.accel_model.accelmodel Abstract base class for accel models. Accelerated models are neural networks that can be accelerated using dedicated hardware.
Published Date : Dienstag, 15. Oktober 2019

azureml.core.runconfig module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.runconfig Classes for managing run configurations from various sources.
Published Date : Donnerstag, 17. Oktober 2019

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 : Dienstag, 15. Oktober 2019

azureml.accel.models.Densenet121 class - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models.densenet121 Float-32 Version of Densenet. This model is in RGB format, and has a scaling factor of 0.017
Published Date : Dienstag, 15. Oktober 2019

azureml.core.dataset module - Azure Machine Learning Python

https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.dataset Manages the interaction with Azure Machine Learning Datasets. This module provides functionality for consuming raw data, managing data, and performing actions on data in Azure Machine Learning service. Use the class in this module to create datasets along with the functionality in the package, which contains the supporting classes and . To get started with datasets, see the article Add &ampamp; register datasets.
Published Date : Dienstag, 15. Oktober 2019