Tag Archives: AzureML

Using miniCRAN in Azure ML

Using miniCRAN in Azure ML
// R-bloggers

(This article was first published on Revolutions, and kindly contributed to R-bloggers)

by Michele Usuelli
Microsoft Data Scientist

Azure Machine Learning Studio is a drag-and-drop tool to deploy data-driven solutions. It contains pre-built items including data preparation tools and Machine Learning algorithms. In addition, it allows to include R and Python custom scripts.

In order to build powerful R tools, you might want to use some packages from the CRAN repository. Azure ML already contains just a few packages, so you might need to include some others. There are 7000+ packages out of which you will need just a few. For this purpose, you can use the miniCRAN package which creates a local repository containing a selection of packages and their dependencies.

You can get a free Azure ML subscription following this:

Azure Data Factory Updates: Integration with Azure Machine Learning!

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Azure Data Factory released a powerful enhancement enabling integration with Azure Machine Learning. You now have the ability to run your finished Azure Machine Learning models from within your data factory pipelines – so you can repeatedly feed your trained scoring models with data from multiple sources. The seamless integration enables batch prediction scenarios such as identifying possible loan defaults, determining sentiment, and analyzing customer behavior patterns.

Get started quickly by creating an AzureMLLinkedService and AzureMLBatchScoringActivity to invoke your batch Azure Machine Learning models in a data pipeline.

Webinar: Predictive Analytics with Azure Machine Learning

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Take your business to the next level with Machine Learning. Up to now, the power of machine learning has only been utilized by a very small number of organizations and has required highly trained and specialized data scientists.

Discover how Azure Machine Learning makes predictive analytics directly accessible to business analysts and domain experts, reducing complexity and broadening participation through better tooling.

Learn how you can build a predictive analytics model using machine learning over data and hear about real-world customer success stories.

Python Tools for Visual Studio now integrates with Azure Machine Learning

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Two languages are closely associated with Data Science today – R and Python. In Azure ML we’ve supported R for some time – and very soon we’ll add full Python support as well. This includes a world-class Python experience in Visual Studio, in Azure ML Studio and in the browser via Jupyter/IPython. As a first step, we’re excited to announce that the Python Tools for Visual Studio (PTVS) team has added features to integrate with Azure Machine Learning APIs hosted in the cloud.

I’m also happy to announce that PTVS 2.1 RTW was recently released and is available from codeplex. Note that this is an officially supported OSS plug-in. When installed into the Professional version of Visual Studio (free, available here), you’ll have a powerful Python centric Data Science IDE that is completely free. We believe powerful open source tools such as PTVS will greatly empower developers and help democratize frontier technologies such as machine learning and advanced analytics. 

Sentiment Analysis In Excel With Azure Machine Learning And Power Query

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You may have seen Joseph Sirosh’s blog post last week about the ability to publish Azure Machine Learning models to the Azure Marketplace, and that MS have published a number of APIs there already. There’s a newExcel add-in that can be used with these APIs but I noticed that at least one of them, the Sentiment AnalysisAPI, can be used direct from Power Query too.

To do this, the first thing you need to do is to go to the Azure Marketplace, sign in with your Microsoft account, and subscribe to the Lexicon Based Sentiment Analysis API. The docs say you get 25000 transactions free per month although there doesn’t appear to be a way to pay for more; that said the number of transactions remaining shown on my account kept resetting, so maybe there is no limit. The API itself is straightforward: pass it a sentence to evaluate and it will return a score between –1 and 1, where 1 represents a positive sentiment and –1 is a negative sentiment. For example, the sentence “I had a good day” returns the value 1:

In-depth introduction to Machine Learning in 15 hours of expert videos

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Chapter 1: Introduction (slidesplaylist)

Chapter 2: Statistical Learning (slidesplaylist)

Chapter 3: Linear Regression (slidesplaylist)

Chapter 4: Classification (slidesplaylist)

Chapter 5: Resampling Methods (slidesplaylist)

Chapter 6: Linear Model Selection and Regularization (slidesplaylist)

Chapter 7: Moving Beyond Linearity (slidesplaylist)

Chapter 8: Tree-Based Methods (slidesplaylist)

Chapter 9: Support Vector Machines (slidesplaylist)

Chapter 10: Unsupervised Learning (slidesplaylist)

Interviews (playlist)

AzureML Web Service Parameters

Click to Read: AzureML Web Service APIs are published from Experiments that are built using modules with configurable parameters. There is often a need to change the module behavior during Web Service execution. The Web Service Parameters feature enables this functionality.