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@@ -23,7 +23,14 @@ This feature is available in the **Premium** container registry service tier. Fo
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## Prerequisites
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* To use the Azure CLI steps in this article, Azure CLI version 2.2.0 or later is recommended. If you need to install or upgrade, see [Install Azure CLI][azure-cli]. Or run in [Azure Cloud Shell](../cloud-shell/quickstart.md).
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* If you don't already have a container registry, create one (Premium tier required) and push a sample image such as `hello-world` from Docker Hub. For example, use the [Azure portal][quickstart-portal] or the [Azure CLI][quickstart-cli] to create a registry.
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* If you don't already have a container registry, create one (Premium tier required) and push a sample image such as `hello-world` from Docker Hub. For example, use the [Azure portal][quickstart-portal] or the [Azure CLI][quickstart-cli] to create a registry.
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* If you want to configure registry access using a private link in a different Azure subscription, you need to register the resource provider for Azure Container Registry in that subscription. For example:
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```azurecli
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az account set --subscription <Name or ID of subscription of private link>
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az provider register --namespace Microsoft.ContainerRegistry
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```
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The Azure CLI examples in this article use the following environment variables. Substitute values appropriate for your environment. All examples are formatted for the Bash shell:
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You can view items you published through the Azure AI Gallery website UI. Users can view both public and unlisted solutions, projects, experiments, and other published items:
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1.Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.The profile page displays all items published to the gallery, including unlisted entries.
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1.Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.The profile page displays all items published to the gallery, including unlisted entries.
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## Use the AI Gallery Catalog API to view your data
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### Get an author ID
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The author ID is based on the email address used when publishing to the Azure AI Gallery. It doesn't change:
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1.Sign in to [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.The URL in the address bar displays the alphanumeric ID following `authorId=`. For example, for the URL:
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1.Sign in to [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.The URL in the address bar displays the alphanumeric ID following `authorId=`. For example, for the URL:
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To get an access token, you need to inspect the `DataLabAccessToken` header of an HTTP request the browser makes to the Catalog API while logged in:
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1.Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.Open the browser Developer Tools pane by pressing F12, select the Network tab, and refresh the page.
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1.Sign in to the [Azure AI Gallery](https://gallery.azure.ai/).
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2.Click the profile picture in the top-right corner, and then the account name to load your profile page.
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3.Open the browser Developer Tools pane by pressing F12, select the Network tab, and refresh the page.
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4. Filter requests on the string *catalog* by typing into the Filter text box.
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5.In requests to the URL `https://catalog.cortanaanalytics.com/entities`, find a GET request and select the *Headers* tab. Scroll down to the *Request Headers* section.
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6.Under the header `DataLabAccessToken` is the alphanumeric token. To help keep your data secure, don't share this token.
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5.In requests to the URL `https://catalog.cortanaanalytics.com/entities`, find a GET request and select the *Headers* tab. Scroll down to the *Request Headers* section.
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6.Under the header `DataLabAccessToken` is the alphanumeric token. To help keep your data secure, don't share this token.
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### View user information
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Using the author ID you got in the previous steps, view information in a user's profile by replacing `[AuthorId]` in the following URL:
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This query displays only public entities. To view all your entities, including unlisted ones, provide the access token obtained from the previous section.
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1.Using a tool like [Postman](https://www.getpostman.com), create an HTTP GET request to the catalog URL as described in [Get your access token](#get-your-access-token).
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2.Create an HTTP request header called `DataLabAccessToken`, with the value set to the access token.
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3.Submit the HTTP request.
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1.Using a tool like [Postman](https://www.getpostman.com), create an HTTP GET request to the catalog URL as described in [Get your access token](#get-your-access-token).
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2.Create an HTTP request header called `DataLabAccessToken`, with the value set to the access token.
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3.Submit the HTTP request.
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> [!TIP]
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> If unlisted entities are not showing up in responses from the Catalog API, the user may have an invalid or expired access token. Sign out of the Azure AI Gallery, and then repeat the steps in [Get your access token](#get-your-access-token) to renew the token.
This topic describes how to choose the right hyperparameter set for an algorithm in Azure Machine Learning Studio (classic). Most machine learning algorithms have parameters to set. When you train a model, you need to provide values for those parameters. The efficacy of the trained model depends on the model parameters that you choose. The process of finding the optimal set of parameters is known as *model selection*.
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Alternately, you can define the maximum and minimum points of the grid and the total number of points to be generated with **Use Range Builder**. By default, the parameter values are generated on a linear scale. But if **Log Scale** is checked, the values are generated in the log scale (that is, the ratio of the adjacent points is constant instead of their difference). For integer parameters, you can define a range by using a hyphen. For example, “1-10” means that all integers between 1 and 10 (both inclusive) form the parameter set. A mixed mode is also supported. For example, the parameter set “1-10, 20, 50” would include integers 1-10, 20, and 50.
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Alternately, you can define the maximum and minimum points of the grid and the total number of points to be generated with **Use Range Builder**. By default, the parameter values are generated on a linear scale. But if **Log Scale** is checked, the values are generated in the log scale (that is, the ratio of the adjacent points is constant instead of their difference). For integer parameters, you can define a range by using a hyphen. For example, "1-10" means that all integers between 1 and 10 (both inclusive) form the parameter set. A mixed mode is also supported. For example, the parameter set "1-10, 20, 50" would include integers 1-10, 20, and 50.
This article presents a reference implementation of a customer churn analysis project that is built by using Azure Machine Learning Studio (classic). In this article, we discuss associated generic models for holistically solving the problem of industrial customer churn. We also measure the accuracy of models that are built by using Machine Learning, and assess directions for further development.
Net# is a language developed by Microsoft that is used to define complex neural network architectures such as deep neural networks or convolutions of arbitrary dimensions. You can use complex structures to improve learning on data such as image, video, or audio.
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You can use a Net# architecture specification in these contexts:
Download this easy-to-understand infographic overview of machine learning basics to learn about popular algorithms used to answer common machine learning questions. Algorithm examples help the machine learning beginner understand which algorithms to use and what they're used for.
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## Popular algorithms in Machine Learning Studio (classic)
Once you deploy an Azure Machine Learning Studio (classic) predictive model as a Web service, you can use a REST API to send it data and get predictions. You can send the data in real-time or in batch mode.
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You can find more information about how to create and deploy a Machine Learning Web service using Machine Learning Studio (classic) here:
Azure Machine Learning Studio (classic) gives you the tools you need to develop a predictive analytics model and then operationalize it by deploying it as an Azure web service.
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To do this, you use Studio (classic) to create an experiment - called a *training experiment* - where you train, score, and edit your model. Once you're satisfied, you get your model ready to deploy by converting your training experiment to a *predictive experiment* that's configured to score user data.
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