Tags : #AzureML

15

Nov

Exciting AI Platform & Tools Announcements from Microsoft
Exciting AI Platform & Tools Announcements from Microsoft

Re-posted from the Azure blog.

We made some exciting AI-related announcements at Microsoft Connect(); 2017 earlier today. Specifically, we talked about how we’re making it even easier for developers and data scientists to infuse AI into new and existing apps with these new capabilities:

Azure IoT Edge integration in Azure ML.
A new Azure Databricks service that combines the best of Databricks and Azure for Spark-based analytics.
A new Visual Studio Tools for AI development environment with Azure ML integration.

With these updates, the Microsoft AI platform – summarized in the picture below – now offers comprehensive cloud-based, on-premises, and edge support – in other words, all the infrastructure, tools, frameworks, services and solutions needed by developers, data scientists and businesses to infuse AI into their products and services.

Check out the original post here to learn about these updates in more detail, and about the innovative ways in which our customers and putting these new AI technologies to use in the real world.

ML Blog Team

Resources:

Visit http://www.azure.com/ai to learn more about how AI can augment and empower digital transformation efforts.
Visit the AI School to get up to speed with all the relevant AI technologies.

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15

Nov

Artificial Intelligence and Machine Learning on the Cutting Edge
Artificial Intelligence and Machine Learning on the Cutting Edge

This post is authored by Ted Way, Senior Program Manager at Microsoft.

Today we are excited to announce the ability to bring intelligence to the edge with the integration of Azure Machine Learning and Azure IoT Edge. Businesses today understand how artificial intelligence (AI) and machine learning (ML) are critical to help them go from telling the “what happened” story to the “what will happen” and “how can we make it happen” story. The challenge is how to apply AI and ML to data that cannot make it to the cloud, for instance due to data sovereignty, privacy, bandwidth or other issues. With this integration, all models created using Azure Machine Learning can now be deployed to any IoT gateways and devices with the Azure IoT Edge runtime. These models are deployed to the edge in the form of containers and can run on very small footprint devices.

Intelligent Edge
Use Cases

There many use cases for the intelligent edge, where a model is trained in the cloud and then deployed to an edge device. For example, a hospital wants to use AI to identify lung cancer on CT scans. Due to patient privacy and bandwidth limitations, a large CT

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15

Nov

Artificial Intelligence and Machine Learning on the Cutting Edge
Artificial Intelligence and Machine Learning on the Cutting Edge

This post is authored by Ted Way, Senior Program Manager at Microsoft.

Today we are excited to announce the ability to bring intelligence to the edge with the integration of Azure Machine Learning and Azure IoT Edge. Businesses today understand how artificial intelligence (AI) and machine learning (ML) are critical to help them go from telling the “what happened” story to the “what will happen” and “how can we make it happen” story. The challenge is how to apply AI and ML to data that cannot make it to the cloud, for instance due to data sovereignty, privacy, bandwidth or other issues. With this integration, all models created using Azure Machine Learning can now be deployed to any IoT gateways and devices with the Azure IoT Edge runtime. These models are deployed to the edge in the form of containers and can run on very small footprint devices.

Intelligent Edge
Use Cases

There many use cases for the intelligent edge, where a model is trained in the cloud and then deployed to an edge device. For example, a hospital wants to use AI to identify lung cancer on CT scans. Due to patient privacy and bandwidth limitations, a large CT

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09

Nov

Free Webinars in November – Learn from Big Data & Machine Learning Applications in Healthcare

Join us for a set of exciting webinars starting next Tuesday, November 14th, at which we’ll show you how data science and machine learning are being applied in the medical field. You will be able to take the learnings from these webinars to use Azure Machine Learning, Azure Data Lake and Hadoop Spark clusters in big data and ML solutions that are relevant to your organization or to your customers. You will also learn how technologies like ML and AI can be effectively used as a tool for cost control, which is especially critical in the healthcare industry.

More on each session below. Be sure to click the links attached to the titles of these sessions to reserve your spot today. All sessions are entirely free, of course.

Data Science and Machine Learning in Healthcare: A Population Health Management Solution

In this webinar, we talk about two things: How data science and ML can be used to manage and control escalating healthcare costs, and how to create a Population Health Management solution using state-of-the-art Azure Data Lake (ADL) Analytics. While outlining the Population Health Management solution, we will introduce you to ADL Analytics with R integration and show you how

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09

Nov

Free Webinars in November – Learn from Big Data & Machine Learning Applications in Healthcare

Join us for a set of exciting webinars starting next Tuesday, November 14th, at which we’ll show you how data science and machine learning are being applied in the medical field. You will be able to take the learnings from these webinars to use Azure Machine Learning, Azure Data Lake and Hadoop Spark clusters in big data and ML solutions that are relevant to your organization or to your customers. You will also learn how technologies like ML and AI can be effectively used as a tool for cost control, which is especially critical in the healthcare industry.

More on each session below. Be sure to click the links attached to the titles of these sessions to reserve your spot today. All sessions are entirely free, of course.

Data Science and Machine Learning in Healthcare: A Population Health Management Solution

In this webinar, we talk about two things: How data science and ML can be used to manage and control escalating healthcare costs, and how to create a Population Health Management solution using state-of-the-art Azure Data Lake (ADL) Analytics. While outlining the Population Health Management solution, we will introduce you to ADL Analytics with R integration and show you how

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26

Oct

Anaconda and Microsoft Partner to Offer Python and R for Powerful Machine Learning

This post was authored by Nagesh Pabbisetty, Partner Director of Program Management, Microsoft Machine Learning Services.

Recently, at Strata Data Conference in New York City, Microsoft and Anaconda announced an exciting partnership to make Anaconda Python distribution into SQL Server, Machine Learning Server, Azure Machine Learning, and Visual Studio to deliver real-time insights. In addition, Anaconda will be distributing Microsoft R. Let’s take a deeper look at this exciting new partnership.

Microsoft is committed to helping developers build AI powered applications by enabling them to do machine learning and AI wherever their data is. SQL Server 2017 includes Machine Learning Services — enterprise grade in-database machine learning capabilities with R and Python languages. Machine Learning Server enables customers to do scalable machine learning using R or Python on standalone Windows and Linux servers, Hadoop clusters and Azure data platforms.

Anaconda is the leading distribution of Python leveraged by millions of users today. A strong partnership with this popular Python distribution for data science further strengthens Microsoft’s goal of building tools to empower every organization to build their own AI capabilities.

Microsoft and Anaconda built a customized Anaconda distribution – Anaconda for Microsoft for doing machine learning with Microsoft products and

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26

Oct

Anaconda and Microsoft Partner to Offer Python and R for Powerful Machine Learning

This post was authored by Nagesh Pabbisetty, Partner Director of Program Management, Microsoft Machine Learning Services.

Recently, at Strata Data Conference in New York City, Microsoft and Anaconda announced an exciting partnership to make Anaconda Python distribution into SQL Server, Machine Learning Server, Azure Machine Learning, and Visual Studio to deliver real-time insights. In addition, Anaconda will be distributing Microsoft R. Let’s take a deeper look at this exciting new partnership.

Microsoft is committed to helping developers build AI powered applications by enabling them to do machine learning and AI wherever their data is. SQL Server 2017 includes Machine Learning Services — enterprise grade in-database machine learning capabilities with R and Python languages. Machine Learning Server enables customers to do scalable machine learning using R or Python on standalone Windows and Linux servers, Hadoop clusters and Azure data platforms.

Anaconda is the leading distribution of Python leveraged by millions of users today. A strong partnership with this popular Python distribution for data science further strengthens Microsoft’s goal of building tools to empower every organization to build their own AI capabilities.

Microsoft and Anaconda built a customized Anaconda distribution – Anaconda for Microsoft for doing machine learning with Microsoft products and

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25

Oct

AI for Education: Individualized Code Feedback for Thousands of Students

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft.

There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can’t offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student’s assignment contains mistakes, that student—within seconds—receives a message specific to their code submission. Beyond just informing the student that their program doesn’t work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them. Students are happy to get fast, focused guidance so they can concentrate on learning new skills rather than on just troubleshooting.

Learning from Thousands of Students

Ke Wang is the Microsoft Research Intern who has led this project since summer 2016. Paul Pardi, Principal Content Publishing Manager in Microsoft Learning, initiated the project because his team was looking to better teach the thousands of students who take their Massive Open Online Courses (MOOCs) through a partnership with edX.org. The idea was to see more students completing their courses with the help of

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25

Oct

AI for Education: Individualized Code Feedback for Thousands of Students

This post is authored by Matthew Calder, Senior Business Strategy Manager, and Ke Wang, Research Intern at Microsoft.

There are more than 9,000 students enrolled in the Microsoft Introduction to C# course on edX.org. Although course staff can’t offer the type of guidance available in an on-campus classroom setting, students can receive personalized help, thanks to a project from Microsoft Research. When a student’s assignment contains mistakes, that student—within seconds—receives a message specific to their code submission. Beyond just informing the student that their program doesn’t work, Microsoft has created a tool which automatically generates feedback that precisely identifies errors and even hints at how to correct them. Students are happy to get fast, focused guidance so they can concentrate on learning new skills rather than on just troubleshooting.

Learning from Thousands of Students

Ke Wang is the Microsoft Research Intern who has led this project since summer 2016. Paul Pardi, Principal Content Publishing Manager in Microsoft Learning, initiated the project because his team was looking to better teach the thousands of students who take their Massive Open Online Courses (MOOCs) through a partnership with edX.org. The idea was to see more students completing their courses with the help of

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29

Mar

Microsoft Makes Big Data and Analytics Easier in the Cloud

This post is by Joseph Sirosh, Corporate Vice President of the Data Group at Microsoft.

This week I’m joining thousands of people attending Strata + Hadoop World in San Jose to explore the technology and business of big data and data science. As part of our participation in the conference, we are announcing several important investments to continue delivering on our commitment to make big data processing and analytics simpler and more accessible:

Advanced analytics at scale with R Server for HDInsight and the latest version of Spark for HDInsight are now available in preview: Customers can leverage their existing R skills and reuse current code to run at scale. R Server for HDInsight offers popular scalable R algorithms and the ability to parallelize any existing R function. We are also releasing the latest version of Spark for HDInsight, which can deliver 7x performance over MapReduce for most analytics. These capabilities give our customers the ability to train and run advanced analytics and ML models on larger datasets, and much faster than previously possible in the cloud. Out-of-the-box application integration, providing easier access to popular big data apps: Customers can now discover and deploy popular big data applications with HDInsight…

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