Machine Learning announcement
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Link
We have a new dedicated forum for Azure Cognitive Services.
Please update your bookmarks to point to: https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureCognitiveService
Thank you.
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Link
We have opened a new forum (https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureMachineLearningService) dedicated to Azure Machine Learning service.
Please direct all Azure Machine Learning service (and the retiring Azure Machine Learning workbench) related questions to the new forum.
Thank you.
Azure Machine Learning team
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Link
We have released support for Azure Document DB as a data source in Azure Machine Learning. You can use the existing "Azure DocumentDB" connection option in the Import Data module to read data from Azure DocumentDB for your experiment.
For more information, please see the DocumentDB section of the Import Data module. -
Link
New Module: Extract Key Phrases from Text
You can use this module to extract key talking points from text. As an input, the module takes a dataset that must have a text string column from which the key-phrases are extracted.
The module takes the language of the text records as input parameter. Supported languages include Dutch, English, French, German, Italian and Spanish. You can also use a language column that specifies the language of each record, as produced by Detect Languages module.
The output consists of comma-separated lists of key phrases for each record in input. The key phrases can be used to summarize a corpus of documents, or as features for a machine learning model.
Updated Module: Preprocess Text
- You can specify a language through a language column, as produced by Detect Languages module.
- Following three preprocessing options have been added: Expand verb contractions, Normalize backslashes to slashes, and Split tokens on special characters. Previously, these transformations were done automatically.
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Link
We are pleased to announce the availability of Azure Machine Learning Workspaces and Web Service Plans for all our Azure Machine Learning users through the Azure Portal. Azure Machine Learning users can now create and manage Standard workspaces through the Azure Portal. In addition, users will also be able to create Web Service Pricing Plans. These plans are used when deploying web services and provide included quantities of operationalized compute at a single, predictable monthly cost.
Create your Standard Azure Machine Learning workspace now by going to https://portal.azure.com. Log in with the credentials that you use for accessing your Azure Subscription(s). Click on +New | Data + Analytics | Machine Learning Workspace.
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Link
We are pleased to announce significant new capabilities for text analytics in Azure Machine Learning Studio.
The new features include following modules:
- Detect Languages
- Identify language of each record in input file from large number of languages.
- Preprocess Text
- Clean and simplify text to make it more easy to featurize.
- Extract N-Gram Features from Text
- Create N-gram feature vectors from long text strings, and select only the most important features.
- Latent Dirichlet Allocation
- Group text into categories using topic modeling.
These modules allow you to build models to solve text classification problems, such as support ticket routing or sentiment analysis. You can pre-process text in multiple languages, and then create features from your text data. Operationalization of models is fully supported.
The modules complement the existing capabilities for Feature Hashing, Vowpal Wabbit based high-dimensional models, and text analytics through R and Python scripting.
For more details, visit MSDN documentation and Cortana Intelligence Gallery.
- Detect Languages
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Link
There is an issue impacting the "New" web service option for deploying web services from Predictive Experiments in Azure ML. We are working on resolving the issue, and a result have disabled the feature until the feature is fully functional. To access web services created the new process, please browse to https://services.azureml.net and sign in to view your web services. Sorry for any inconvenience this issue may cause.
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Link
We have released support for Azure SQL Data Warehouse as a data source and a destination in Azure Machine Learning. You can use the existing "Azure SQL Database" connection options in the Reader and Writer modules to read from and write to Azure SQL Data Warehouse. When using the Writer module, the destination tables must already exist in the SQL Data Warehouse.
For more information, please see How to Use Azure ML with Azure SQL Data Warehouse
Please refer to SQL Data Warehouse Reference to learn more about the product and the Transact-SQL language details.
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Link
Visualization of tree models such as Boosted Decision Trees is now available in Azure Machine Learning Studio. To view the trees, train the model, and click Visualize on the output of Train Model module.
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Link
Announcing the Availability of an Azure Virtual Machine Image with Popular Data Science Tools
Microsoft Data Group is happy to announce the immediate availability of a Windows Server 2012 based custom virtual machine image on the Azure marketplace containing several tools that can be used by data scientists and developers for advanced analytics. Through Azure’s world-wide cloud infrastructure, customers now have on-demand access to a data science development environment they can use to derive insights from their data, build predictive models and intelligent applications. The virtual machine saves developers’ time from having to discover and install the tools individually. Hosting the data science machine on Azure gains you high availability and a consistent set of tools used across your data science team.
The data science VM comes with several popular tools pre-installed like Revolution R Open, Anaconda Python distribution including Jupyter notebook server, Visual Studio Community Edition, Power BI Desktop, SQL Server Express edition and Azure SDK. Once you provision your virtual machine from this image you can get started with data exploration and modeling right away. The data on the virtual machine is stored on the cloud and highly available. You have full administrative access to the virtual machine and can install additional software as needed. There is no separate software fee to use the VM image. You only pay for actual hardware compute usage of the virtual machine depending on the size of the virtual machine you are provisioning this VM on. You
The data science virtual machine helps you create an analytics environment where you can rapidly build advanced analytics solutions for deployment to the cloud, on-premises or in a hybrid environment.
You can find the data science virtual machine and the Azure hardware compute pricing at: https://azure.microsoft.com/en-us/marketplace/partners/microsoft-ads/standard-data-science-vm/
More information about the virtual machine can be found at: https://azure.microsoft.com/en-us/documentation/articles/machine-learning-data-science-provision-vm/
If you are new to Azure, you can try the data science virtual machine for free via a 30-day Azure free trial by visitinghttps://azure.microsoft.com/en-us/pricing/free-trial/
We encourage you to try the data science virtual machine to jumpstart your analytics project and provide us feedback on how we can better serve your analytics needs.
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Link
We are happy to announce that we have released Azure ML in our Western Europe datacenter (Amsterdam). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlwelaunch.
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Link
We are happy to announce that we have released Azure ML in our SouthEast Asia datacenter (Singapore). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlasialaunch.
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Link
We are happy to announce that we have released Azure Active Directory (AAD) support in Azure ML. Now you can log in with any arbitrary Azure AD account (work or school account), in addition to, Microsoft accounts (LiveID), and invite other Azure AD users to your workspace. For more information, click here: http://blogs.technet.com/b/machinelearning/archive/2015/09/02/logging-on-to-azure-ml-with-your-work-or-school-account.aspx.
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Link
A free Excel add-in that you can use with web services published from Azure Machine Learning is now available. You can use this add-in for request/response predictions or batch predictions, work in Windows or the browser, share workbooks with your co-workers, and call multiple web services all within a single spreadsheet. Go to http://aka.ms/amlexcelhelp for help or ask a question here.
To try it out, open and download sample Excel worksheets that already contain web services:
http://aka.ms/amlexcel-sample-1
http://aka.ms/amlexcel-sample-2
You may use the add-in directly in the browser using Excel Online or opening the file in Excel 2013 or later on Windows. Copy the file to your own OneDrive account if you want to edit it.
Feature highlights
- Connect to multiple web services in one Excel workbook
- Choose from RRS or BES
- Supports single or no input, and single, multiple, or no outputs
For sample 1 (text sentiment analysis): http://aka.ms/amlexcel-sample-1
1.) Highlight cells A1 to A12
2.) Click the range selector button (the selection Sheet1!$A$1:$A:$12 should automatically be populated)
3.) Click OK in the Select Data dialog box
4.) Type “B1” in the output1 text box
5.) Click the Predict button
6.) This web service takes some time to process the text, so please be patient and wait for a minute. When it’s done, you should see the sentiment predictions and scores in columns B and C.
For sample 2 (Titanic survivor predictor): http://aka.ms/amlexcel-sample-2
1.) Highlight cells A1 to G11
2.) Click the range selector button (the selection Sheet1!$A$1:$G:$11 should automatically be populated)
3.) Click OK in the Select Data dialog box
4.) Type “H1” in the output1 text box
5.) Click the Predict button
6.) When it’s done, you should see the predictions and scores in columns H and I
To add your own web service:
1.) In the Excel add-in, go to the Web Services section (if you are in the Predict section, click the back arrow to go to the list of web services)
2.) Click Add Web Service
3.) In Azure ML Studio, click the WEB SERVICES section in the left pane, and then select the web service
4.) Copy the API key for the web service
5.) Paste the API key into the Excel add-in text box labeled API key
6.) On the DASHBOARD tab for the web service, click the REQUEST/RESPONSE link
7.) Look for the OData Endpoint Address section. Copy the URL and paste that into the text box labeled URL in the Excel add-in\
8.) Click Add
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Link
On July 24th, 2015, Microsoft announced the Preview Availability release of Jupyter Notebooks in Azure Machine Learning Studio.
Azure Machine Learning Studio is a powerful canvas for the composition of Machine Learning Experiments and subsequent operationalization and consumption. It provides an easy to use, yet powerful, drag-drop style of creating Experiments. But sometimes you need a good old “REPL” that allows you to have a tight loop where you enter some script code and get a response. We are delighted to announce that we’ve now integrated this functionality into ML Studio through Jupyter Notebooks.
Jupyter enables the concept of “executable documents” with support for mixed code, markdown and inline graphics. It’s one of the most important innovations in the Data Science and Technical Computing space in recent years. You now have full access to its power from any OS, from any modern browser directly from inside the Azure Machine Learning Studio.
In addition to authoring capabilities above, we are also enabling publishing AzureML web services directly from the Jupyter Notebook. We are also extending this capability to the Jupyter Notebooks running locally outside of AzureML Studio. This allows you to publish any function, including those creating ML models, to be published as a web service directly from the Jupyter Notebook running on your machine. The result is an AzureML web service API that can be called to perform functions or predictions from client applications in real time and over the internet. -
Link
Announcing the availability of the SDK for AzureML Batch Execution Service (BES)
The AzureML BES SDK is now available for download and installation as a NuGet package on NuGet.org (http://www.nuget.org/packages/Microsoft.Azure.MachineLearning/).
The SDK wraps the BES sample code with additional functions to simplify the consumption of BES APIs.
Documentation is available after installing the SDK package in Visual Studio. The BES documentation has also been updated with sample code and guidance on using the SDK.
We are looking forward to hearing your feedback and comments on the SDK to help improve it.
Thanks,
AzureML Team
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Link
We have posted a demo of the Retraining APIs on Codeplex.com. The demo uses the new APIs to programmatically retrain a trained model. Here is the link to the Demo.
Please take a look and let us know if you have questions or comments.
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0 Votes
Reading data(.csv) directly from OneDrive to Machine Learning VM
How can I directly read a dataset from OneDrive to train a model on Machine Learning VM ? -
0 Votes
AzureML time series model cannot recognize feature values on inference
Same post on Stackoverflow: https://stackoverflow.com/questions/59204799/azureml-time-series-model-cannot-recognize-feature-values-on-inference I have ...Proposed | 5 Replies | 340 Views | Created by Martin H. Normark - Thursday, December 5, 2019 11:54 PM | Last reply by GiftA-MSFT - 17 hours 52 minutes ago -
0 Votes
Help. Issue Running My Experiment due to module import error - Input Port Dataset is Unconnected.
Hello, I'm new here and trying to run a simple experiment as part of the intro to Data Sci Specialization with Microsoft. Issue is that whenever I import the Math ...Proposed | 3 Replies | 520 Views | Created by Black9t - Saturday, January 5, 2019 12:51 PM | Last reply by TunaKılıc - Friday, December 13, 2019 8:09 PM -
0 Votes
Deploy custom R script as web service Azure ML Studio
Hi all! I have an R script which takes as input an excel file with two columns containing dates-values and it gives as output 3 dates with the corresponding ... -
3 Votes
"Failed to start the kernel - XSRF cookie does not match POST argument"
I'm not sure this is the right forum to post this question - but I don't know where else to go. BTW, I tried posting it as an issue over at Github, but received no response. In ...Answered | 9 Replies | 5252 Views | Created by JackTrade - Wednesday, August 29, 2018 1:02 PM | Last reply by manu mr - Friday, December 13, 2019 6:12 AM -
1 Votes
Delete trained model
How to remove a trained model in Azure Machine Learning?Proposed | 3 Replies | 696 Views | Created by erralberto - Saturday, January 10, 2015 7:47 PM | Last reply by RohitMungi-MSFT - Friday, December 13, 2019 5:02 AM -
0 Votes
Problem for call Microsoft face api
API Reference However, I don't know how to use HTTP/Request2.php that make I can not run the sample. I have searched some solutions which install the pear for ...Proposed | 3 Replies | 202 Views | Created by Calling Microsoft face api - Monday, December 9, 2019 3:51 PM | Last reply by GiftA-MSFT - Wednesday, December 11, 2019 11:29 PM -
0 Votes
Converting user language to Machine understandable SQL queries
Hello, I have Members database where I have details of the members like Member name, country, age, salary, educational qualification, etc... Using Microsoft ...Proposed | 1 Replies | 151 Views | Created by Abdul222 - Tuesday, December 10, 2019 11:36 AM | Last reply by GiftA-MSFT - Wednesday, December 11, 2019 10:35 PM -
8 Votes
QnA Maker's Metadata: what is it and how should I use it?
So, I am pretty with Microsoft Azure's tools. I was using Azure's QnA Maker Preview to develop a FAQ bot. But just today, Microsoft released a huuuuge update, making this servisse much more powerful, ...Answered | 9 Replies | 3197 Views | Created by PedroSouzaNeto - Monday, May 7, 2018 8:30 PM | Last reply by Xtianus - Tuesday, December 10, 2019 5:16 PM -
0 Votes
Need to analyze input CSV files and determine whether input file is good or bad w.r.t it's data
Hi Team, We have a scenario where we need to implement an Artificial Intelligence solution which will evaluate the input data file of my Azure Data Factory pipeline and let us know ...Unanswered | 1 Replies | 139 Views | Created by Dileep Mittapalli - Monday, December 9, 2019 6:28 PM | Last reply by RohitMungi-MSFT - Tuesday, December 10, 2019 7:58 AM -
1 Votes
Train Matchbox recommender. Training dataset of user-item-rating triples contains invalid data. . ( Error 0018 )
Hello! I loaded my data from SQL server successfully and edited rating to integer then removed duplicated rows. However, when it came to train matchbox recommender returning error ...Unanswered | 3 Replies | 540 Views | Created by Bukhara - Sunday, March 4, 2018 7:07 AM | Last reply by SinTheta - Tuesday, December 10, 2019 6:19 AM -
0 Votes
Machine learning?
I have set up an Azure Kubernetes platform which works well. I can publish some hello-world containers and connect. Great. Now I would like to integrate with ...Proposed | 1 Replies | 184 Views | Created by Emmechan - Monday, December 9, 2019 12:23 PM | Last reply by GiftA-MSFT - Tuesday, December 10, 2019 12:40 AM -
0 Votes
Matchbox Error 35 web service
I have an issue that I can't resolve, I get this error (see below) despite having provided features for that user (both ratings and features). The experiment run ...Proposed | 1 Replies | 191 Views | Created by kyril_wi - Friday, December 6, 2019 1:51 PM | Last reply by RohitMungi-MSFT - Monday, December 9, 2019 9:22 AM -
0 Votes
Error Tuning hyperparameters on specific dataset
Hello, I am getting an error when training a model. It worked fine until I added more rows to my dataset. Let me know if I need to provide anything ...Answered | 1 Replies | 219 Views | Created by arrowren - Monday, December 2, 2019 6:26 PM | Last reply by arrowren - Monday, December 2, 2019 7:35 PM -
0 Votes
Filter Based Feature Selection : Error 0023: Input dataset has unsupported target column "CH01"
Hello, I created prediction experiment and when testing it I am getting following error: Error Message: Filter Based Feature Selection : Error 0023: Input dataset ...Answered | 1 Replies | 219 Views | Created by Arkady-Karasin - Monday, December 2, 2019 1:06 PM | Last reply by Arkady-Karasin - Monday, December 2, 2019 2:31 PM -
0 Votes
LibraryExecutionError - testing a web service published via Rstudio
% select(-Species) )Answered | 3 Replies | 360 Views | Created by Colin Cortechs - Wednesday, November 27, 2019 10:59 AM | Last reply by RohitMungi-MSFT - Monday, December 2, 2019 6:32 AM -
0 Votes
Time Series Json Example Help
I have deployed a model that was a regression and using postman have posted to the endpoint using the following Json and I get a response back which ...Unanswered | 3 Replies | 352 Views | Created by Mike874399 - Tuesday, November 26, 2019 5:28 PM | Last reply by Mike874399 - Wednesday, November 27, 2019 10:54 AM -
3 Votes
Unable to deploy model created with AutomatedML
Testing this service out with a linear regression. I am able to run the session just fine and create the possible models. However, the deployment does not work, either when selecting to deploy a ...Proposed | 9 Replies | 522 Views | Created by Bowman74 - Saturday, November 16, 2019 6:55 PM | Last reply by Mike874399 - Tuesday, November 26, 2019 5:23 PM -
0 Votes
script_params for an Estimator
Dear all, I'm going through some samples of creating an estimator in Azure AML, for ...Answered | 2 Replies | 319 Views | Created by Luis Molina Martinez - Monday, November 25, 2019 4:38 PM | Last reply by Luis Molina Martinez - Tuesday, November 26, 2019 7:13 AM -
1 Votes
Getting this odd error when I go to set up my web service
Hi, So I am getting the following error: AFx Library library exception: table: The data set being scored must contain all ...Answered | 4 Replies | 461 Views | Created by dzitam - Wednesday, September 26, 2018 8:51 PM | Last reply by Lidemar - Tuesday, November 26, 2019 5:50 AM - Items 1 to 20 of 4592 Next ›
Machine Learning announcement
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Link
We have a new dedicated forum for Azure Cognitive Services.
Please update your bookmarks to point to: https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureCognitiveService
Thank you.
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Link
We have opened a new forum (https://social.msdn.microsoft.com/Forums/en-US/home?forum=AzureMachineLearningService) dedicated to Azure Machine Learning service.
Please direct all Azure Machine Learning service (and the retiring Azure Machine Learning workbench) related questions to the new forum.
Thank you.
Azure Machine Learning team
-
Link
We have released support for Azure Document DB as a data source in Azure Machine Learning. You can use the existing "Azure DocumentDB" connection option in the Import Data module to read data from Azure DocumentDB for your experiment.
For more information, please see the DocumentDB section of the Import Data module. -
Link
New Module: Extract Key Phrases from Text
You can use this module to extract key talking points from text. As an input, the module takes a dataset that must have a text string column from which the key-phrases are extracted.
The module takes the language of the text records as input parameter. Supported languages include Dutch, English, French, German, Italian and Spanish. You can also use a language column that specifies the language of each record, as produced by Detect Languages module.
The output consists of comma-separated lists of key phrases for each record in input. The key phrases can be used to summarize a corpus of documents, or as features for a machine learning model.
Updated Module: Preprocess Text
- You can specify a language through a language column, as produced by Detect Languages module.
- Following three preprocessing options have been added: Expand verb contractions, Normalize backslashes to slashes, and Split tokens on special characters. Previously, these transformations were done automatically.
-
Link
We are pleased to announce the availability of Azure Machine Learning Workspaces and Web Service Plans for all our Azure Machine Learning users through the Azure Portal. Azure Machine Learning users can now create and manage Standard workspaces through the Azure Portal. In addition, users will also be able to create Web Service Pricing Plans. These plans are used when deploying web services and provide included quantities of operationalized compute at a single, predictable monthly cost.
Create your Standard Azure Machine Learning workspace now by going to https://portal.azure.com. Log in with the credentials that you use for accessing your Azure Subscription(s). Click on +New | Data + Analytics | Machine Learning Workspace.
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Link
We are pleased to announce significant new capabilities for text analytics in Azure Machine Learning Studio.
The new features include following modules:
- Detect Languages
- Identify language of each record in input file from large number of languages.
- Preprocess Text
- Clean and simplify text to make it more easy to featurize.
- Extract N-Gram Features from Text
- Create N-gram feature vectors from long text strings, and select only the most important features.
- Latent Dirichlet Allocation
- Group text into categories using topic modeling.
These modules allow you to build models to solve text classification problems, such as support ticket routing or sentiment analysis. You can pre-process text in multiple languages, and then create features from your text data. Operationalization of models is fully supported.
The modules complement the existing capabilities for Feature Hashing, Vowpal Wabbit based high-dimensional models, and text analytics through R and Python scripting.
For more details, visit MSDN documentation and Cortana Intelligence Gallery.
- Detect Languages
-
Link
There is an issue impacting the "New" web service option for deploying web services from Predictive Experiments in Azure ML. We are working on resolving the issue, and a result have disabled the feature until the feature is fully functional. To access web services created the new process, please browse to https://services.azureml.net and sign in to view your web services. Sorry for any inconvenience this issue may cause.
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Link
We have released support for Azure SQL Data Warehouse as a data source and a destination in Azure Machine Learning. You can use the existing "Azure SQL Database" connection options in the Reader and Writer modules to read from and write to Azure SQL Data Warehouse. When using the Writer module, the destination tables must already exist in the SQL Data Warehouse.
For more information, please see How to Use Azure ML with Azure SQL Data Warehouse
Please refer to SQL Data Warehouse Reference to learn more about the product and the Transact-SQL language details.
-
Link
Visualization of tree models such as Boosted Decision Trees is now available in Azure Machine Learning Studio. To view the trees, train the model, and click Visualize on the output of Train Model module.
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Link
Announcing the Availability of an Azure Virtual Machine Image with Popular Data Science Tools
Microsoft Data Group is happy to announce the immediate availability of a Windows Server 2012 based custom virtual machine image on the Azure marketplace containing several tools that can be used by data scientists and developers for advanced analytics. Through Azure’s world-wide cloud infrastructure, customers now have on-demand access to a data science development environment they can use to derive insights from their data, build predictive models and intelligent applications. The virtual machine saves developers’ time from having to discover and install the tools individually. Hosting the data science machine on Azure gains you high availability and a consistent set of tools used across your data science team.
The data science VM comes with several popular tools pre-installed like Revolution R Open, Anaconda Python distribution including Jupyter notebook server, Visual Studio Community Edition, Power BI Desktop, SQL Server Express edition and Azure SDK. Once you provision your virtual machine from this image you can get started with data exploration and modeling right away. The data on the virtual machine is stored on the cloud and highly available. You have full administrative access to the virtual machine and can install additional software as needed. There is no separate software fee to use the VM image. You only pay for actual hardware compute usage of the virtual machine depending on the size of the virtual machine you are provisioning this VM on. You
The data science virtual machine helps you create an analytics environment where you can rapidly build advanced analytics solutions for deployment to the cloud, on-premises or in a hybrid environment.
You can find the data science virtual machine and the Azure hardware compute pricing at: https://azure.microsoft.com/en-us/marketplace/partners/microsoft-ads/standard-data-science-vm/
More information about the virtual machine can be found at: https://azure.microsoft.com/en-us/documentation/articles/machine-learning-data-science-provision-vm/
If you are new to Azure, you can try the data science virtual machine for free via a 30-day Azure free trial by visitinghttps://azure.microsoft.com/en-us/pricing/free-trial/
We encourage you to try the data science virtual machine to jumpstart your analytics project and provide us feedback on how we can better serve your analytics needs.
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Link
We are happy to announce that we have released Azure ML in our Western Europe datacenter (Amsterdam). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlwelaunch.
-
Link
We are happy to announce that we have released Azure ML in our SouthEast Asia datacenter (Singapore). Now you can create workspaces in this datacenter. For more information, click here: http://aka.ms/mlasialaunch.
-
Link
We are happy to announce that we have released Azure Active Directory (AAD) support in Azure ML. Now you can log in with any arbitrary Azure AD account (work or school account), in addition to, Microsoft accounts (LiveID), and invite other Azure AD users to your workspace. For more information, click here: http://blogs.technet.com/b/machinelearning/archive/2015/09/02/logging-on-to-azure-ml-with-your-work-or-school-account.aspx.
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Link
A free Excel add-in that you can use with web services published from Azure Machine Learning is now available. You can use this add-in for request/response predictions or batch predictions, work in Windows or the browser, share workbooks with your co-workers, and call multiple web services all within a single spreadsheet. Go to http://aka.ms/amlexcelhelp for help or ask a question here.
To try it out, open and download sample Excel worksheets that already contain web services:
http://aka.ms/amlexcel-sample-1
http://aka.ms/amlexcel-sample-2
You may use the add-in directly in the browser using Excel Online or opening the file in Excel 2013 or later on Windows. Copy the file to your own OneDrive account if you want to edit it.
Feature highlights
- Connect to multiple web services in one Excel workbook
- Choose from RRS or BES
- Supports single or no input, and single, multiple, or no outputs
For sample 1 (text sentiment analysis): http://aka.ms/amlexcel-sample-1
1.) Highlight cells A1 to A12
2.) Click the range selector button (the selection Sheet1!$A$1:$A:$12 should automatically be populated)
3.) Click OK in the Select Data dialog box
4.) Type “B1” in the output1 text box
5.) Click the Predict button
6.) This web service takes some time to process the text, so please be patient and wait for a minute. When it’s done, you should see the sentiment predictions and scores in columns B and C.
For sample 2 (Titanic survivor predictor): http://aka.ms/amlexcel-sample-2
1.) Highlight cells A1 to G11
2.) Click the range selector button (the selection Sheet1!$A$1:$G:$11 should automatically be populated)
3.) Click OK in the Select Data dialog box
4.) Type “H1” in the output1 text box
5.) Click the Predict button
6.) When it’s done, you should see the predictions and scores in columns H and I
To add your own web service:
1.) In the Excel add-in, go to the Web Services section (if you are in the Predict section, click the back arrow to go to the list of web services)
2.) Click Add Web Service
3.) In Azure ML Studio, click the WEB SERVICES section in the left pane, and then select the web service
4.) Copy the API key for the web service
5.) Paste the API key into the Excel add-in text box labeled API key
6.) On the DASHBOARD tab for the web service, click the REQUEST/RESPONSE link
7.) Look for the OData Endpoint Address section. Copy the URL and paste that into the text box labeled URL in the Excel add-in\
8.) Click Add
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Link
On July 24th, 2015, Microsoft announced the Preview Availability release of Jupyter Notebooks in Azure Machine Learning Studio.
Azure Machine Learning Studio is a powerful canvas for the composition of Machine Learning Experiments and subsequent operationalization and consumption. It provides an easy to use, yet powerful, drag-drop style of creating Experiments. But sometimes you need a good old “REPL” that allows you to have a tight loop where you enter some script code and get a response. We are delighted to announce that we’ve now integrated this functionality into ML Studio through Jupyter Notebooks.
Jupyter enables the concept of “executable documents” with support for mixed code, markdown and inline graphics. It’s one of the most important innovations in the Data Science and Technical Computing space in recent years. You now have full access to its power from any OS, from any modern browser directly from inside the Azure Machine Learning Studio.
In addition to authoring capabilities above, we are also enabling publishing AzureML web services directly from the Jupyter Notebook. We are also extending this capability to the Jupyter Notebooks running locally outside of AzureML Studio. This allows you to publish any function, including those creating ML models, to be published as a web service directly from the Jupyter Notebook running on your machine. The result is an AzureML web service API that can be called to perform functions or predictions from client applications in real time and over the internet. -
Link
Announcing the availability of the SDK for AzureML Batch Execution Service (BES)
The AzureML BES SDK is now available for download and installation as a NuGet package on NuGet.org (http://www.nuget.org/packages/Microsoft.Azure.MachineLearning/).
The SDK wraps the BES sample code with additional functions to simplify the consumption of BES APIs.
Documentation is available after installing the SDK package in Visual Studio. The BES documentation has also been updated with sample code and guidance on using the SDK.
We are looking forward to hearing your feedback and comments on the SDK to help improve it.
Thanks,
AzureML Team
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Link
We have posted a demo of the Retraining APIs on Codeplex.com. The demo uses the new APIs to programmatically retrain a trained model. Here is the link to the Demo.
Please take a look and let us know if you have questions or comments.