Tags : #AzureML

06

Feb

Free: Microsoft AI Bootcamp Materials for Emerging & Pro Developers

This post is authored by Chris Testa-O’Neill, Applied Data Scientist in the Microsoft Cloud AI team.

Artificial Intelligence (AI) is proving to be a massively disruptive force, one that is leading to the digital transformation of businesses at a faster pace than most of us would have imagined. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to provide the best platform and tools to make them successful. You can read more Microsoft’s approach to AI here.

In keeping with our mission, we are currently running a series of popular AI boot camps around the world. This post shares more information about these boot camps and provides links for you to access these materials and start building your own AI apps in a self-paced way.

Our bootcamps have two target audience profiles, the emerging AI developer and the professional AI developer, and our curriculum is primarily oriented towards these two personas. The first two days of the bootcamp are aimed at the emerging AI developer. The target profile here is an IT developer who is yet to use Microsoft AI tools and APIs to infuse intelligence into their business applications.

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30

Jan

Hearing AI: Getting Started with Deep Learning for Audio on Azure
Hearing AI: Getting Started with Deep Learning for Audio on Azure

This post is authored by Xiaoyong Zhu, Program Manager, Max Kaznady, Senior Data Scientist, and Gilbert Hendry, Senior Data Scientist, at Microsoft.

There are an increasing number of useful applications of machine learning and Artificial Intelligence in the domain of audio, such as in home surveillance (e.g. detecting glass breaking and alarm events); security (e.g. detecting sounds of explosions and gun shots); driverless cars (sound event detection for increased safety); predictive maintenance (forecast machine failures in a manufacturing process based on vibrations); for real-time translation in Skype and even for music synthesis.

The human brain processes such a wide variety of sounds so effortlessly – be it the bark of puppies, audible alarms from smoke or carbon monoxide detectors, or people talking loudly in a coffee shop – that most of us tend to take this faculty for granted. But what if we could apply AI to help the hearing impaired achieve something similar? That would be something special.

So, is AI ready to help the hearing impaired understand and react to the world around them?

In this post, we describe how to train a Deep Learning model on Microsoft Azure for sound event detection on the Urban Sounds dataset,

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23

Jan

Care and Feeding of Predictive Maintenance Solutions
Care and Feeding of Predictive Maintenance Solutions

This post is authored by John Ehrlinger, Data Scientist at Microsoft.

Microsoft has recently launched Azure Machine Learning services (AML) to public preview. The updated services include a Workbench application plus command-line tools to assist in developing and managing machine learning solutions through the entire data science life cycle. An Experimentation Service handles the execution of ML experiments and provides project management, Git integration, access control, roaming, and sharing of work. The Model Management Service allows data scientists and dev-ops teams to deploy predictive models into a wide variety of environments. Model versions and lineage are tracked from training runs to deployments while being stored, registered, and managed in the cloud.

Once AML Workbench is installed, the app connects to a Gallery of prebuilt real world data science scenario projects to help new users explore Azure ML, as well as give users a jump start on their specific data science scenarios.

The AML gallery currently contains two predictive maintenance example scenarios:

A PySpark implementation using a random forest of decision trees classifiers:
https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-predictive-maintenance
A deep learning approach using an LSTM classifier:
https://docs.microsoft.com/en-us/azure/machine-learning/preview/scenario-deep-learning-for-predictive-maintenance

This post is written specifically to prepare users interested in using their own data to deploy customized predictive maintenance scenarios.

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22

Jan

Custom Vision Service: Code-Free Automated Machine Learning for Image Classification

Re-posted from the Microsoft Azure blog.

Artificial Intelligence (AI) is a hugely disruptive force, one that is powering much of the digital transformation businesses are going through in recent times. At Microsoft, our mission is to bring AI to every developer and every organization on the planet, and to help businesses augment human ingenuity in unique and differentiated ways.

Developers and data scientists are at the heart of this transformation and the mission for the Microsoft AI platform is to offer the very best tools to make them successful in this journey. These include tools for automating machine learning through the pre-built AI capabilities we offer for vision, speech, language, knowledge and search in the form of the Microsoft Cognitive Services, which are enabling a rich variety of customer scenarios. As an example, when we announced the general availability of our conversational AI tools last month, we showcased innovative applications from leading edge customers such as Molson Coors, UPS and many others.

We continue to innovate on our AI platform at a rapid pace and wish to make AI easy by bringing capabilities such as transfer learning and automated machine learning to developers.

In this context, we are excited

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18

Jan

Deep Learning & Computer Vision in the Microsoft Azure Cloud
Deep Learning & Computer Vision in the Microsoft Azure Cloud

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition.

Introduction

I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners — we start off with the fundamentals of neural networks and machine learning and by the end of the program you’re training state-of-the-art networks on the ImageNet dataset from scratch. My second goal was to provide a book that included:

Practical walkthroughs that present solutions to actual, real-world deep learning classification problems.
Hands-on tutorials (with accompanying code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well.
A no-nonsense teaching style that cuts through all the cruft and helps you on your path to deep learning + computer vision mastery for visual recognition.

Along the way I quickly realized that a stumbling block for many readers is configuring their development environment — especially true for

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18

Jan

Deep Learning & Computer Vision in the Microsoft Azure Cloud
Deep Learning & Computer Vision in the Microsoft Azure Cloud

This is the first in a multi-part series by guest blogger Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python, and he recently finished authoring a new book on deep learning for computer vision and image recognition.

Introduction

I had two goals when I set out to write my new book, Deep Learning for Computer Vision with Python. The first was to create a book/self-study program that was accessible to both novices and experienced researchers and practitioners — we start off with the fundamentals of neural networks and machine learning and by the end of the program you’re training state-of-the-art networks on the ImageNet dataset from scratch. My second goal was to provide a book that included:

Practical walkthroughs that present solutions to actual, real-world deep learning classification problems.
Hands-on tutorials (with accompanying code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well.
A no-nonsense teaching style that cuts through all the cruft and helps you on your path to deep learning + computer vision mastery for visual recognition.

Along the way I quickly realized that a stumbling block for many readers is configuring their development environment — especially true for

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10

Jan

How to Run Large-Scale Educational Workshops in Deep Learning & Data Science

This post is authored by Gopi Kumar, Principal Program Manager, and Paul Shealy, Senior Software Engineer at Microsoft.

With the rise of Artificial Intelligence, the need to rapidly train a large number of data scientists and AI developers has never been more urgent. Microsoft is always looking for efficient ways to educate employees and customers on AI and make them more productive when using these new capabilities. Aside from numerous technical conferences that we host and sponsor, we also offer the AI School and a range of tools such as the Data Science Virtual Machine, Visual Studio Tools for AI, Azure Machine Learning, Microsoft ML Server, and Batch AI to help developers and data scientists become more productive around building their intelligent AI-infused apps.

Pulling together deep learning workshops for a large number of students, however, can be a time consuming, error prone, and costly exercise. Furthermore, technical issues with the environment setup and compatibility problems during the workshops impede learning and cause student dissatisfaction. These workshops typically have participants bring their laptops and have them download and install new software. However, with the wide range of laptop platforms (Windows, Mac, Linux), numerous configurations, and version conflicts with existing software, workshops

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03

Jan

Hello 2018 | Recap of “Top 10” Posts of 2017
Hello 2018  |  Recap of “Top 10” Posts of 2017

As we ring in the new year, we’d like to kick things off in our usual fashion – with a quick recap of our most popular posts from the year just concluded. So here are our “Top 10” posts from 2017, sorted in increasing order of readership – enjoy!

10. Quick-Start Guide to the Data Science Bowl Lung Cancer Detection Challenge, Using Deep Learning, Microsoft Cognitive Toolkit and Azure GPU VMs

Lung cancer – which is the leading cancer when it comes to mortality in both women and men in the US – suffers from a low rate of early diagnosis. The Data Science Bowl competition aimed to help by having participants use machine learning to determine whether CT scans of the lung have cancerous lesions or not. Success in the competition required that data scientists get started quickly and iterate rapidly. Through this post, we showed how to compute features of scanned images with a pre-trained Convolutional Neural Network (CNN), and use these features to classify scans as cancerous or not using a boosted tree – all within one hour.

9. Machine Learning for Developers – How to Build Intelligent Apps & Services

Traditionally, developers would build rules-based engines

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22

Dec

ICYMI: Recent Microsoft AI Platform Updates, Including in ONNX, Deep Learning, Video Indexer & More

Four recent Microsoft posts about AI developments, just in case you missed it.

1. Getting Started with Microsoft AI – MSDN Article

This MSDN article, co-authored by Joseph Sirosh and Wee Hyong Tok, provides a nice summary of all the capabilities offered by the Microsoft AI platform and how you can get started today. From Cognitive Services that help you to build intelligent apps, to customizing state-of-the-state computer vision deep learning models, to building deep learning models of your own with Azure Machine Learning, the Microsoft AI platform is open, flexible and provides developers the right tools that are the best suited for their wide range of scenarios and skills levels. Click here or on the image below to read the original article, on MSDN.

2. Announcing ONNX 1.0 – An Open Ecosystem for AI

Microsoft firmly believes in bringing AI advances to all developers, on any platform, using any language, and with an open AI ecosystem that helps us ensure that the fruits of AI are broadly accessible. In December, we announced that Open Neural Network Exchange (ONNX), an open source model representation for interoperability and innovation in the AI ecosystem co-developed by Microsoft, is production-ready. The ONNX

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19

Dec

How We Share the Latest AI & ML Developments Within Microsoft
How We Share the Latest AI & ML Developments Within Microsoft

We recently concluded the Fall 2018 edition of the Machine Learning, AI & Data Science (MLADS) conference, Microsoft’s largest internal gathering of employees focused specifically on these areas. This latest edition was the eighth in a popular series that we launched back in 2014. Over 3,500 employees tuned into the sold-out conference, both in person in Redmond and over livestream throughout the world, and thousands more will tune into MLADS session recordings over coming weeks and months.

As application of AI and ML explode both within Microsoft and in our external products and services, the growth of our community interest groups catering to these areas has been very rapid. The MLADS conference itself is unique in that it is almost entirely driven by enthusiastic community volunteers – a band of employees unified in its passion for AI and ML, and a desire to network and learn from one another. The “call for content” that goes out for this conference series routinely gets several hundreds of submissions, and our volunteer team helps triage these submissions and curate the best ones for our event.

The fall 2018 conference featured over 95 talk sessions, 20 tutorials and 65 poster/demo sessions covering a gamut of

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