This post is authored by Anusua Trivedi, Carlos Pessoa, Vivek Gupta & Wee Hyong Tok from the Cloud AI Platform team at Microsoft.
AI has emerged as one of the most disruptive forces behind digital transformation and it is revolutionizing the way we live and work. AI-powered experiences are augmenting human capabilities and transforming how we live, work, and play – and they have enormous potential in allowing us to lead healthier lives as well.
AI is empowering clinicians with deep insights that are helping them make better decisions, and the potential to save lives and money is tremendous. At Microsoft, the Health NExT project is looking at innovative approaches to fuse research, AI and industry expertise to enable a new wave of healthcare innovations. The Microsoft AI platform empowers every developer to innovate and accelerate the development of real-time intelligent apps on edge devices. There are a couple of advantages of running intelligent real-time apps on edge devices – you get:
Lowered latency, for local decision making.
Reduced reliance on internet connectivity.
Imagine environments where there’s limited or no connectivity, whether it’s because of lack of communications infrastructure or because of the sensitivity of the
This post is authored by Said Bleik, Senior Data Scientist at Microsoft.
In the IoT world, it’s not uncommon that you’d want to monitor thousands of devices across different sites to ensure normal behavior. Devices can be as small as microcontrollers or as big as aircraft engines and might have sensors attached to them to collect various types of measurements that are of interest. These measurements often carry signals that indicate whether the devices are functioning as expected or not. Sensor data can be used to train predictive models that serve as alarm systems or device monitors that warn when a malfunction or failure is imminent.
In what follows, I will walk you through a simple scalable solution that can handle thousands or even millions of sensors in an IoT setting. I will show how you can train many anomaly detection models (one model for each sensor) in parallel using Azure’s Batch AI. I’ve created a complete training pipeline that includes: a local data simulation app to generate data, an Azure Event Hubs data ingestion service, an Azure Stream Analytics service for real-time processing/aggregation of the readings, an Azure SQL Database to store the processed data, an Azure Batch AI
This is the third post in a three-part series by guest blogger, Adrian Rosebrock. Adrian writes at PyImageSearch.com about computer vision and deep learning using Python. He recently finished authoring a new book on deep learning for computer vision and image recognition.
In the final part in this series, I want to address a question I received from Mason, a PyImageSearch reader, soon after I published the first post in the series:
I noticed that you said tested all of the code for your new deep learning book on the Microsoft Data Science Virtual Machine (DSVM). Does that include the chapters on training networks on the ImageNet dataset as well? I work at a university and we’re allocating our budget for both physical hardware in the lab and cloud-based GPU instances. Could you share your experience training large networks on the DSVM? Thanks.
Mason poses a great question – is it possible, or even advisable, to use cloud-based solutions such as the Microsoft DSVM to train state-of-the-art neural networks on large datasets?
Most deep learning practitioners are familiar with the “Hello, World” equivalents on the MNIST and CIFAR-10 datasets. These examples are excellent to get started as they require minimal (if any) investment in GPUs.
This post is authored by Daniel Grecoe, Senior Software Engineer at Microsoft.
Replica sets and pods and nodes…oh my!
Microsoft has created very powerful and customizable tools for the professional data scientist with the Azure Machine Learning Workbench. The tool is used to create container images for Machine Learning or Artificial Intelligence models and exposing them as a REST API endpoints deployed and managed on an Azure Container Service. This is a very different approach than Azure Machine Learning Studio. In Studio, a model is created by the data scientist who then publishes that model as a REST API endpoint with the click of a button and the entire service is then managed by Azure.
The flexibility of the Azure ML Workbench relinquishes control to the scientist and Dev Ops teams with regards to development and operationalization. That flexibility transfers responsibility of the backend service to DevOps who must that the Kubernetes cluster is scaled appropriately for the desired load and responsiveness.
This post discusses scaling a Kubernetes cluster with Azure Container Service and walks through many of the considerations of operationalizing a container created with Azure ML Workbench on Azure Container Service but also covers basic Kubernetes considerations to
This post is authored by Shaheen Gauher, Data Scientist at Microsoft.
Data scientists who have been hearing a lot about Docker must be wondering whether it is, in fact, the best thing ever since sliced bread. If you too are wondering what the fuss is all about, or how to leverage Docker in your data science work (especially for deep learning projects) you’re in the right place. In this post, I present a short tutorial on how Docker can give your deep learning projects a jump start. In the process you will learn the basics of how to interact with Docker containers and create custom Docker images for your AI workloads. As a data scientist, I find Docker containers to be especially helpful as my development environment for deep learning projects for the reasons outlined below.
If you have tried to install and set up a deep learning framework (e.g. CNTK, Tensorflow etc.) on your machine you will agree that it is challenging, to say the least. The proverbial stars need to align to make sure the dependencies and requirements are satisfied for all the different frameworks that you want to explore and experiment with. Getting the right anaconda distribution,
This post is authored by Ilia Karmanov, Mathew Salvaris, Miguel Fierro, Danielle Dean, all Data Scientists at Microsoft.
With this blog post, we are releasing a full version 1.0 of this repo, open-source on GitHub at: https://github.com/ilkarman/DeepLearningFrameworks.
We believe deep-learning frameworks are like languages: Sure, many people speak English, but each language serves its own purpose. We have created common code for several different network structures and executed it across many different frameworks. Our idea was to a create a Rosetta Stone of deep-learning frameworks – assuming you know one well, to help anyone leverage any framework. Situations may arise where a paper publishes code in another framework or the whole pipeline is in another language. Instead of writing a model from scratch in your favourite framework it may be easier to just use the “foreign” language.
We want to extend our gratitude to the CNTK, Pytorch, Chainer, Caffe2 and Knet teams, and everyone else from the open-source community who contributed to the repo over the past few months.
In summary, our goals with this release were to create:
A Rosetta Stone of deep-learning frameworks to allow data-scientists to easily leverage their expertise from one framework to another.
This post is authored by Rosane Maffei Vallim, Program Manager, and Wilson Lee, Senior Software Engineer at Microsoft.
Artificial Intelligence (AI) with deep learning and machine learning algorithms are changing the way we solve variety of problems from manufacturing to biomedical industries. The applications that can benefit from the power of AI are endless.
With the Windows Machine Learning (Windows ML) API, as .NET developers, we can now leverage the ONNX models that have been trained by data scientists and use them to develop intelligent applications that run AI locally. In this blog post, we will give an overview of what Windows ML can do for you; show you how to use ONNX in your UWP application; and introduce you to the Windows Machine Learning Explorer sample application that generically bootstraps ML models to allow users to dynamically select different models within the same application.
Channel 9’s AI Show for this blog post can be found here.
Windows Machine Learning Explorer sample application code for this blog post can be found here.
Why is Windows ML + ONNX Great News for .NET Developers?
Earlier this month, we announced the AI Platform for Windows Developers.
Windows ML is an
This post was authored by Mary Wahl, Kolya Malkin, Siyu Yang, Patrick Flickinger, Wee Hyong Tok, Lucas Joppa, and Nebojsa Jojic, representing the Microsoft Research and AI for Earth teams.
Last week Microsoft launched the Geo AI Data Science Virtual Machine (DSVM), an Azure VM type specially tailored to data scientists and analysts that manage geospatial data. To support the Geo AI DSVM launch, we are sharing sample code and methods for our joint land cover mapping project with the Chesapeake Conservancy and ESRI. We have used Microsoft’s Cognitive Toolkit (CNTK) to train a deep neural network-based semantic segmentation model that assigns land cover labels from aerial imagery. By reducing cost and speeding up land cover map construction, such models will enable finer-resolution timecourses to track processes like deforestation and urbanization. This blog post describes the motivation behind our work and the approach we’ve taken to land cover mapping. If you prefer to get started right away, please head straight to our GitHub repository to find our instructions and materials.
Motivation for the land cover mapping use case
The Chesapeake Conservancy is a non-profit organization charged with monitoring natural resources in the Chesapeake Bay watershed, a >165,000 square kilometer region
This post was authored by Eric Boyd, CVP, AI Data & Infrastructure.
Today Microsoft is announcing the next major update to Windows will include the ability to run Open Neural Network Exchange (ONNX) models natively with hardware acceleration. This brings 100s of millions of Windows devices, ranging from IoT edge devices to HoloLens to 2-in-1s and desktop PCs, into the ONNX ecosystem. Data scientists and developers creating AI models will be able to deploy their innovations to this large user base. And every developer building apps on Windows 10 will be able to use AI models to deliver more powerful and engaging experiences.
ONNX is an open source model representation for interoperability and innovation in the AI ecosystem. We helped start ONNX last September, added support from many other companies, and launched ONNX 1.0 in December with Facebook and Amazon Web Services. With the ONNX format, developers can choose the right framework for their task, framework authors can focus on innovative enhancements and hardware vendors can streamline optimizations.
Thanks to ONNX-ML, Windows supports both classic machine learning and deep learning, enabling a spectrum of AI models and scenarios. Developers can obtain ONNX models to include in their apps
This blog post is co-authored by Xiaoyong Zhu, George Iordanescu and Ilia Karmanov, Data Scientists at Microsoft, and Mazen Zawaideh, Radiologist Resident at the University of Washington Medical Center.
Artificial Intelligence (AI) has emerged as one of the most disruptive forces behind digital transformation that is revolutionizing the way we live and work. This applies to the field of healthcare and medicine too, where AI is accelerating change and empowering physicians to achieve more. At Microsoft, the Health NExT project is looking at innovative approaches to fuse research, AI and industry expertise to enable a new wave of healthcare innovations. The Microsoft AI platform empowers every developer to innovate and accelerate the development of intelligent apps. AI-powered experiences augment human capabilities and transform how we live, work, and play – and have enormous potential in allowing us to lead healthier lives.
Take the task of detecting diseases from chest x-ray images, for instance. This is a challenging task, one that requires consultation with an expert radiologist. However, two-thirds of the world’s population lacks access to trained radiologists, even when imaging equipment is readily available. The lack of image interpretation by experts may lead to delayed diagnosis and could potentially