You have heard the hype around serverless computing, read countless online articles about its benefits, and heard industry pundits find new adjectives to describe its pathbreaking nature on a frequent basis. Are you now ready to dig into what you can actually do with real code using serverless computing cloud services like Azure Functions? If so, you should download and read the free Azure Serverless Computing Cookbook that describes with rich code examples, how to solve common problems using serverless Azure Functions.
However, if you need a little more motivation, read on.
Let us get the basics out of the way first. Serverless computing enables:
Full abstraction of servers: Focusing on your application code, not on servers running the code. Instant event-driven scalability: Not worrying about scaling up infrastructure as traffic grows. Pay-per-use: Paying only for the time your code is running and the resources it consumes
However, as a developer there is so much more to it that you should care about.
Your cloud journey can start small
You don’t really have to bet your entire application portfolio on this new way of building software all at once. The good thing about the Functions-as-a-Service (FaaS) model provided
This post is authored by Frederico Pravatta Rezende, Senior Product Marketing Manager, CADD & AI.
Is your organization prepared for the General Data Protection Regulation (GDPR)?
If your company does business in Europe, you’ll need to be aware of this new privacy law, which is set to bolster data protections for individuals living within the European Union (EU) starting on May 25, 2018.
The GDPR introduces several specific rights for EU residents, such as the right to access their personal data, correct inaccuracies in their data, erase data, object to the processing of their data, and to obtain a copy of their data. It aims to ensure that personal data is protected no matter where it’s sent, processed, or stored.
For your organization, this means taking a fresh look at how you control exposure to personal data, employ security mechanisms to protect personal data, detect and notify supervisory authorities of breaches within a timely manner, keep records of data-processing activities, and document risks and security measures.
The cost of non-compliance is high, reaching up to €20 million or 4 percent of the worldwide annual revenue of the prior fiscal year, whichever is higher.
Microsoft is committed to the GDPR, and
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,
https://powerbi.microsoft.com/en-us/blog/data-bi-summit-announces-keynote-the-next-era-of-analytics-with-microsoft-power-bi/Source: https://powerbi.microsoft.com/en-us/blog/data-bi-summit-announces-keynote-the-next-era-of-analytics-with-microsoft-power-bi/ The Data & BI Summit, held in Dublin, Ireland 24-26 April is quickly approaching and we are happy to announce our keynote session and speaker details:Keynote speaker: John DoyleJohn Doyle,…
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.
We continue to expand the Azure Marketplace ecosystem. In February 2018, 81 new offers successfully met the onboarding criteria and went live.
See details of the new offers below:
Sensitive Data Discovery and De-Id Tool (SDDT): SDDT simplifies and automates organization’s compliance with the GLBA, HIPAA, PCI, GDPR.
Actian Vector Analytic Database Community Edition: Vector is the world’s fastest analytic database designed from ground-up to exploit x86 architecture.
Dyadic EKM Server Image: Dyadic Enterprise Key Management (EKM) lets you manage and control keys in any application deployed in Azure.
Infection Monkey: Open against source attack simulation tool to test the resilience of Azure deployments cyber-attacks.
Maestro Server V6: The power of Profisee Base Server with GRM, SDK, Workflow, and Integrator.
BigDL Spark Deep Learning Library v0.3: Deep Learning framework for distributed
computing leveraging Spark architecture on Xeon CPUs. Feature-parity with TF and Caffe, but with no GPU required.
Informatica Enterprise Data Catalog: Discover and understand data assets across your enterprise with an AI-powered data catalog.
Microsoft and 21Vianet have agreed to extend their partnership to provide world-class public cloud services to Chinese customers. Combining Microsoft’s global technological leadership and 21Vianet’s local operations expertise, Microsoft Azure and Office 365 operated by 21Vianet have achieved unprecedented, robust three-digit growth for nearly four consecutive years in China. This breakthrough cooperation model pioneered by Microsoft and 21Vianet has been recognized as an effective and successful method for a legal and compliant operation of international cloud services in China.
Announced in 2013 and officially launched in March 2014, Microsoft was the first international public cloud provider to bring its cloud technology to China in partnership with 21Vianet. Customers and partners range from established Chinese brands such as Haier, Lenovo, and Huawei to emerging powerhouses such as smartphone manufacturer Xiaomi, bike-share company Mobike, automobile manufacturer BYD, world-leading sporting goods company Amer Sports and Arcplus Data & Innovation Technology, an integrated solutions provider for construction engineering industry.
Office 365 operated by 21Vianet was launched in China in April 2014 and now ranks # 1 in China’s SaaS market. Huawei, Tencent, and Pactera currently use Office 365 operated by 21Vianet to empower their employees and optimize their daily business operations.
Microsoft Azure and
https://powerbi.microsoft.com/en-us/blog/power-bi-service-and-mobile-february-2018-feature-summary/Source: https://powerbi.microsoft.com/en-us/blog/power-bi-service-and-mobile-february-2018-feature-summary/ It’s time again to recap all the features and announcements in Power BI service and mobile in the month of February. Here’s what we released: Automatically install apps, P4 and P5 capacities on READ MORE
https://powerbi.microsoft.com/en-us/blog/power-bi-for-mixed-reality-app-now-available-in-preview/Source: https://powerbi.microsoft.com/en-us/blog/power-bi-for-mixed-reality-app-now-available-in-preview/ In today’s digitally transformed world, data is everywhere. Everybody in an organization, including first line task workers and service engineers, uses data every day to get insights and make the right decisions. Until READ MORE
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