Machine Learning announcement
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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
can turn off the machine from Azure portal when it is not in use to avoid being billed. When you restart the virtual machine from the Azure portal you can continue your development with all your data and files intact. Further augment your analytics on your data science virtual machine by leveraging solutions in Microsoft’s Cortana Analytics Suite.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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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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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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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 VotesError Running Iris Classification With Docker-Spark On AML Workbench
As the title says, I'm trying to run the Iris Classification Sample (https://docs.microsoft.com/en-us/azure/machine-learning/preview/tutorial-classifying-iris-part-2) with Docker-Spark environment on ...Proposed | 5 Replies | 67 Views | Created by Yuheng Fu - Monday, October 02, 2017 10:59 PM | Last reply by Hai Ning - 2 hours 9 minutes ago -
0 VotesAzure CLI command prompt location
Where do I access the Azure CLI command prompt. I don't see the option in the file menu (from the Azure machine learning workbench)Answered | 2 Replies | 30 Views | Created by Tom_at_UDR - 8 hours 36 minutes ago | Last reply by raymondl_msft - 7 hours 14 minutes ago -
0 VotesCannot run Document Collection Analysis in AML Workbench
When I download the document collection analysis project and I run 1_Preprocess_Text.ipynb or preprocessText.py I get following error (from within AML WB or from commandline, same ...Proposed | 3 Replies | 55 Views | Created by DataTeamBE - 12 hours 43 minutes ago | Last reply by Ke-Huang - 9 hours 45 minutes ago -
0 VotesAzure ML Workbench Installation
Hi, I recently got a new PC, just finished updated to the latest version 1703. I tried running the .msi installer but it failed at 20%. The only message that I ...Proposed | 5 Replies | 146 Views | Created by Dai Dao - Thursday, September 28, 2017 7:28 PM | Last reply by SEWARE - 9 hours 59 minutes ago -
0 VotesTensorflow error on cpu
I have created the Data Science vm DS2_V2 (no gpu). When I am running a python script, I am getting error NVIDIA: no NVIDIA devices found E ...Unanswered | 2 Replies | 42 Views | Created by CathyTer - 22 hours 1 minutes ago | Last reply by Paul Shealy [MSFT] - 10 hours 16 minutes ago -
0 VotesHow to Stop an Azure Machine Learning Web Service
I can't seem to stop an Azure Machine Learning Web Service. I have deployed a predictive experiment. It's obviously running because it correctly responds to programmatic requests. ...Proposed | 3 Replies | 53 Views | Created by PeterBruderlin - Tuesday, October 03, 2017 6:40 AM | Last reply by Hai Ning - 12 hours 15 minutes ago -
0 VotesError when clicking on Import Data module
My model was previously working fine. Today, <g class="gr_ gr_22 gr-alert gr_spell gr_inline_cards gr_run_anim ContextualSpelling multiReplace" data-gr-id="22" ... -
0 VotesError running the classifying Iris ML uinsg the AML Workbench
when I try to run the classifying iris ML I get following error: ...AppData\local\AmlWorkbench\Python\lib\site-packages\numpy\core\__init__.py", line 14, in ...Answered | 13 Replies | 134 Views | Created by DataTeamBE - Monday, October 02, 2017 5:04 PM | Last reply by DataTeamBE - Tuesday, October 03, 2017 6:15 PM -
0 VotesWeb service has intermittent latency problems
I have a request-response web service that performs reasonably well most of the time, returning a response inUnanswered | 2 Replies | 71 Views | Created by TonyAndrews - Wednesday, September 27, 2017 6:56 PM | Last reply by TonyAndrews - Tuesday, October 03, 2017 3:47 PM -
0 VotesWhich Azure Machine learning Algorithm should I use to get or suggest top n items?
Hi, I have a problem in which via Azure ML I have to suggest few entities(items) to user based on historic data. Historic data that I have consists of one table called Criteria ...Unanswered | 3 Replies | 92 Views | Created by Yugank Narula - Friday, September 29, 2017 12:36 PM | Last reply by Jaya Mathew - Tuesday, October 03, 2017 2:30 PM -
0 VotesAzure ML, R script, package
Hello, I need to run a R code that use the kohonen package https://cran.r-project.org/web/packages/kohonen/index.html My first code line ... -
0 VotesConnection to Amazon Redshift database with R using RPostgreSQL2 package
Hi, We are trying to use Redshift as source of data for our ML Studio experiments and haven't been able to. We are loading the RPostgreSQL2 R package loaded as a custom package. A ...Proposed | 1 Replies | 111 Views | Created by jclozanoc - Thursday, September 14, 2017 2:06 PM | Last reply by AK - Azure ML - Monday, October 02, 2017 9:26 PM -
0 VotesTrack azure services usage based on Azure Pass code?
Is there any way to track consumption of azure services based on Azure Pass promo code? We're interested in knowing what services people were experimenting with at a hackathon where azure passes were ...Answered | 3 Replies | 27 Views | Created by Track Azure Service Consumption from Azure Pass - Monday, October 02, 2017 5:48 PM | Last reply by Hai Ning - Monday, October 02, 2017 9:17 PM -
0 VotesObtain the R source code for an Azure ML experiment
Hi all, Quite ago I happened to be in a training seminar where the person demonstrated how to obtain the source code of entire experiment in C#, Python and R. I wanted to ask how it ...Proposed | 1 Replies | 32 Views | Created by kumar_s_1 - Monday, October 02, 2017 5:00 PM | Last reply by Hai Ning - Monday, October 02, 2017 7:07 PM -
1 VotesForecasting for multiple intervals in time horizon using an azure ML model
HI, I have created a regression model for forecasting in azure ML using Quantile Forest Regression. The model is performing well. But when I deploy it as webservice I am able to ...Proposed | 1 Replies | 86 Views | Created by Joe Manjiyil - Friday, September 15, 2017 4:15 AM | Last reply by Jaya Mathew - Monday, October 02, 2017 2:23 PM -
0 VotesHow to load a saved model ?
The load model module requires either a URL or the blob storage path (along with name+account) which point to the trained model. Where can I get this URL or path? Also, how do we dynamically load the ...Proposed | 1 Replies | 35 Views | Created by sujay.dsa - Monday, October 02, 2017 2:06 PM | Last reply by Jaya Mathew - Monday, October 02, 2017 2:17 PM -
0 VotesML Studio workspace from 3rd party does not show up in workspace list on https://studio.azureml.net/
I have had a few azure ml workspaces though my own Azure account for a while. Recently I was added as "Contributor" to a new Azure workspace as Contributor. In Azure Portal I can see it ...Answered | 1 Replies | 58 Views | Created by Monria - Saturday, September 30, 2017 7:38 AM | Last reply by Hai Ning - Sunday, October 01, 2017 2:50 AM -
0 VotesAmlWorkbench install failed - disk space
Hi, I attempted the AmlWorkbench installation which hung after about 30 minutes. Inspected the log file and found a disk space error: "9/28/2017 6:02:02 PM 24: Installer [Error] - 0: Install ...Answered | 2 Replies | 66 Views | Created by fhagney - Friday, September 29, 2017 1:37 PM | Last reply by fhagney - Saturday, September 30, 2017 7:45 PM -
0 VotesAccount stuck in queue, no experiments are running
Workspace adfa73f062cc4fd6a208adab5da464ba can you clear my queue? I think i started then deleted too many things can you just fully clear ... -
0 VotesAzure ML Workbench Totally Blank
I'm experiencing an issue with Azure Machine Learning Workbench (the desktop app): the application is totally blank, devoid of content. After entering login credentials, the ...Unanswered | 9 Replies | 189 Views | Created by Brad_Johnson23 - Wednesday, September 27, 2017 12:58 AM | Last reply by Craig Lee - MSFT - Friday, September 29, 2017 4:03 PM - Items 1 to 20 of 2737 Next ›
Machine Learning announcement
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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
can turn off the machine from Azure portal when it is not in use to avoid being billed. When you restart the virtual machine from the Azure portal you can continue your development with all your data and files intact. Further augment your analytics on your data science virtual machine by leveraging solutions in Microsoft’s Cortana Analytics Suite.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.
