Through the Azure AI Camp, the ML practitioner will learn how to use Azure ML, Databricks, ML on the Edge and other Microsoft AI technologies to unlock insights on big datasets and deploy AI services to the cloud and edge. It is designed as a hands-on workshop experience, recommended in instructor-led format or on-demand learning by using the documentation and resources provided for guidance. In this workshop, the following resources will get provisioned. In practice, most are shared amongst an organization or group. For this workshop it will depend upon the Azure Subscription setup.

We are happy to announce that Microsoft and Intel are partnering to bring optimized deep learning frameworks to Azure. These optimizations are available in a new offering on the Azure marketplace called the Intel Optimized Data Science VM for Linux (Ubuntu). Over the last few years, deep learning has become the state of the art for several machine learning and cognitive applications. Deep learning is a machine learning technique that leverages neural networks with multiple layers of non-linear transformations, so that the system can learn from data and build accurate models for a wide range of machine learning problems. Computer vision, language understanding, and speech recognition are all examples of deep learning at play today.

The Azure Data Science Virtual Machine is a customized virtual machine environment on the Microsoft Azure cloud built specifically for doing data science. It has many popular data science and other tools pre-installed and pre-configured to jump-start building intelligent applications for advanced analytics. The Data Science Virtual Machine is available in versions for both Windows and Linux Ubuntu (Azure Machine Learning service is not supported on Linux CentOS). For specific version information and a list of what's included, see Introduction to the Azure Data Science Virtual Machine. The Data Science Virtual Machine is supported as a target for Azure Machine Learning service.

Figure 9: The summary page allows you to review and agree to the contract. Step 7: Wait while the system deploys -- you'll see a convenient notification when your system is ready.

Integrating geography and location information with AI brings a powerful new dimension to understanding the world around us. This has a wide range of applications in a variety of segments, including commercial, governmental, academic or not-for-profit. Geospatial AI provides robust tools for gathering, managing, analyzing and predicting from geographic and location-based data, and powerful visualization that can enable unique insights into the significance of such data. Available today, Microsoft and Esri will be offering the GeoAI Data Science Virtual Machine (DSVM) as part of our Data Science Virtual Machine/Deep Learning Virtual Machine family of products on Azure. This is a result of a collaboration between the two companies and will bring AI, cloud technology and infrastructure, geospatial analytics and visualization together to help create more powerful and intelligent applications.

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. 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. Along the way I quickly realized that a stumbling block for many readers is configuring their development environment -- especially true for those wanted to utilize their GPU(s) and train deep neural networks on massive image datasets (such as ImageNet).

The Ubuntu DSVM is supported as a native VM image in Batch AI. The Ubuntu DSVM comes with many deep learning frameworks, GPU drivers, CUDA, and cuDNN pre-installed, so it is easy to get started with a deep learning project. Data scientists can develop an initial version of a model on a single DSVM, using a smaller dataset, then easily scale out across many DSVMs and larger datasets in Batch AI when ready. Using the same DVM image in Batch AI minimizes the setup time required for your cluster's VMs and reduces incompatibilities between Batch AI and your development environment. Batch AI handles the details of setting up your cluster, can automatically scale up and down based on demand, and supports low-priority VMs for additional cost savings.

A new member has just joined the family of Data Science Virtual Machines on Azure: The Deep Learning Virtual Machine. Like other DSVMs in the family, the Deep Learning VM is a pre-configured environment with all the tools you need for data science and AI development pre-installed. The Deep Learning VM is designed specifically for GPU-enabled instances, and comes with a complete suite of deep learning frameworks including Tensorflow, PyTorch, MXNet, Caffe2 and CNTK. All Data Science Virtual Machines, including the Deep Learning Virtual Machine, are available as Windows and Ubuntu Linux instances, and are free of any software charges: pay only for the infrastructure charge according to the power and size of the instance you choose. An Azure account is required, but you can get started with $200 in free Azure credits here.

Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale. Together, these applications and services help significantly accelerate your data science project development and deployment. Azure Machine Learning fully supports open source technologies. You can execute your experiments in managed environments such as Docker containers and Spark clusters.

The Windows edition of the Data Science Virtual Machine (DSVM), the all-in-one virtual machine image with a wide-collection of open-source and Microsoft data science tools, has been updated to the Windows Server 2016 platform. This update brings built-in support for Docker containers and GPU-based deep learning. While prior editions of the DSVM could access GPU-based capabilities by installing additional components, everything is now configured and ready at launch. The DSVM now includes GPU-enabled builds of popular deep learning frameworks including CNTK, Tensorflow, and MXNET. It also includes Microsoft R Server 9.1, and several machine-learning functions in the MicrosoftML package can also take advantage of GPUs.