Tags : #AzureML

28

Nov

Developing and Operationalizing H2O.ai Models with Azure
Developing and Operationalizing H2O.ai Models with Azure

This post is authored by Daisy Deng, Software Engineer, and Abhinav Mithal, Senior Engineering Manager, at Microsoft.

The focus on machine learning and artificial intelligence has soared over the past few years, even as fast, scalable and reliable ML and AI solutions are increasingly viewed as being vital to business success. H2O.ai has lately been gaining fame in the AI world for its fast in-memory ML algorithms and for easy consumption in production. H2O.ai is designed to provide a fast, scalable, and open source ML platform and it recently added support for deep learning as well. There are many ways to run H2O.ai on Azure. This post provides an overview of how to efficiently develop and operationalize H2O.ai ML models on Azure.

H2O.ai can be deployed in many ways including on a single node, on a multi-node cluster, in a Hadoop cluster and an Apache Spark cluster. H2o.ai is written in Java, so it naturally supports Java APIs. Since the standard Scala backend is a Java VM, H2O.ai also supports the Scala API. It also has rich interfaces for Python and R. The h2o R and h2o Python packages respectively help R and Python users access H2O.ai algorithms and functionality.

16

Nov

On-Demand Webinar – AI Development Using Data Science VMs (DSVM), Deep Learning VMs (DLVM) & Azure Batch AI

This post is authored by Barnam Bora, Program Manager in the Cloud AI group at Microsoft.

Microsoft’s Data Science Virtual Machines (DSVM) and Deep Learning Virtual Machines (DLVM) are a family of popular VM images in Windows Server and Linux flavors that are published on the Azure Marketplace. They have a curated but broad set of pre-configured machine learning and data science tools including pre-loaded samples. DSVM and DLVM are configured and tested to work seamlessly with a plethora of services available on the Microsoft Azure cloud, and they enable a wide array of data analytics scenarios that are being used by many organizations across the globe.

We recently hosted a webinar covering the workflow of building ML and AI -powered solutions in Azure using DSVM, DLVM and related services such as Azure Batch AI and Azure Machine Learning Model Management. The webinar video is available from the link below (requires registration with Microsoft) and more information about the webinar are in the sections that follow.


WATCH: AI development using DSVM/DLVM

Scenarios Covered in the Webinar
Single GPU Node AI Model Training

DSVM and DLVM are great tools to develop, test and deploy AI models and solutions. Data scientists

15

Nov

Using Azure and AI to Explore the JFK Files
Using Azure and AI to Explore the JFK Files

This post is by Corom Thompson, Principal Software Engineer at Microsoft.

On November 22nd, 1963, the President of the United States, John F. Kennedy, was assassinated. He was shot by a lone gunman named Lee Harvey Oswald while driving through the streets of Dallas in his motorcade. The assassination has been the subject of so much controversy that, 25 years ago, an act of Congress mandated that all documents related to the assassination be released this year. The first batch of released files has more than 6,000 documents totaling 34,000 pages, and the last drop of files contains at least twice as many documents. 

We’re all curious to know what’s inside them, but it would take decades to read through these. We approached this problem of gaining insights by using Azure Search and Cognitive Services to extract knowledge from this deluge of documents, using a continuous process that ingests raw documents, enriching them into structured information that enables you to explore the underlying data.

Today, at the Microsoft Connect(); 2017 event, we created the demo web site* shown in Figure 1 below – this is a web application that uses the AzSearch.js library and designed to give you interesting insights into this vast trove of information.

Figure 1 – JFK Files web application for exploring the

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.

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.

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

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

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

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

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