The year 2018 was a banner year for Azure AI as over a million Azure developers, customers, and partners engaged in the conversation on digital transformation. The next generation of AI capabilities are now infused across Microsoft products and services including AI capabilities for Power BI.
Here are the top 10 Azure AI highlights from 2018, across AI Services, tools and frameworks, and infrastructure at a glance:
3. Microsoft is first to enable Cognitive Services in containers.
4. Cognitive Search and basketball
AI tools and frameworks
7. Open Neural Network Exchange (ONNX) runtime is now open source.
10. Project Brainwave, integrated with AML.
With many exciting developments, why are these moments the highlight? Read on, as this blog begins to explain the importance of these moments.
These services span pre-built
As 2018 comes to an end, I look at the technology landscape. I look at the kinds of hybrid scenarios our customers are developing. for example, we see Airbus transforming aerospace with Microsoft Azure Stack and I realize that this year has been amazing for developers that design, develop, and maintain cloud-based apps. Azure Stack has improved support for DevOps practices. You can use Kubernetes containers. You can use API Profiles with Azure Resource Manager and the code of your choice. You can review walkthroughs and tutorials on getting up and running with a development practice using a continuous integration pipeline. With Azure Stack, your apps can be developed in the cloud. You can code once and deploy to environments in Azure or in your local data center.
We are now seeing some of your favorite services from Azure arrive on Azure Stack. The Azure Stack team is also excited to come together with other members of the Azure Edge family, which include Data Box Edge, IoT Edge, and Azure Sphere. If you didn’t get a chance to attend Ignite 2018’s session on the Intellgent Edge check out the “Delivering Intelligent Edge and Microsoft Azure Stack and Data Box” session.
On December 18, 2018, the Azure PowerShell team released the first stable version of “Az,” a new cross-platform PowerShell module that will replace AzureRM. You can install this module by running “Install-Module Az” in an elevated PowerShell prompt.
Since January 2018, PowerShell has been a cross-platform product with the introduction of PowerShell Core. Therefore, it has also become a priority for Azure PowerShell to have cross-platform support. Because of the changes required to support running Azure PowerShell cross-platform, we decided to create a new module rather than make modifications to the existing AzureRM module. Moving forward, all new functionality will be added to the Az module, while AzureRM will only be updated with bug fixes.
Configure Az in your environment
Because both Az and AzureRM use the same dependencies with different versions, it is not possible to run Az and AzureRM side by side in the same PowerShell session. Thus, Az and AzureRM cmdlets cannot be used together in scripts and in interactive sessions. To ensure that a script does not try to import both Az and AzureRM modules in the same session, if you do not have many existing scripts that use AzureRM, we recommend that you remove
In this post, I’ll cover three free resources every developer needs for learning Azure. Dan Fernandez leads the team responsible for bringing our technical documentation and learning resources into a more modern experience that supports new capabilities that were impossible to deliver via MSDN. Recently, I invited Dan to record a few episodes of Azure Friday with Donovan Brown and spend some time showing off the work his team is doing to provide the best doc and learning experience.
1. Microsoft Docs
Last December, I wrote 4 tips for learning Azure in the new year, in which I included links to several resources, including the Azure documentation. In that post, I admit that I did a disservice by glossing over the revolution that Microsoft Docs truly represents – both internally and externally. Not only did it radically change how we create documentation, it improved how you can learn and use Azure.
Did you know that the Azure docs are not only open source, but it’s currently the fastest growing project on GitHub? In this episode, Dan shows off some cool features, a few tips & tricks, how you can contribute, and a
There is a new Azure PowerShell module that is built to harness the power of PowerShell Core and Cloud Shell and maintain compatibility with Windows PowerShell 5.1. Its name is “Az.” Az ensures that Windows PowerShell and PowerShell Core users can get the latest Azure tooling in every PowerShell on every platform. Az also simplifies and normalizes Azure PowerShell cmdlet and module names. Az ships in Azure Cloud Shell and is available from the PowerShell Gallery.
The Az module version 1.0 was released on December 18, 2018, and will be updated on a two-week cadence in 2019, starting with a January 15, 2019 release.
As with all Azure PowerShell modules, Az uses semantic versioning and implements a strict breaking change policy – all breaking changes require advance customer notice and can only occur during breaking change releases.
For complete details on the release, timeline, and compatibility features, check out the Az announcement page.
New features Az runs on Windows PowerShell 5.1 and PowerShell Core (cross-platform) Az is always up to date with the latest tooling for Azure services Az ships in Cloud Shell Az shortens and normalizes cmdlet names – all cmdlets use ‘Az’ as their noun prefix Az simplifies and normalizes
The Developer Economics Q4 2018 survey is here in its 16th edition to shed light on the future of the software industry. Every year more than 40,000 developers around the world participate in this survey, so this is a chance to be part of something big, voice your thoughts, and make your contribution to the developer community. This edition introduces questions about ethics, privacy, security, and project management methodologies in software development.
Is this survey for me?
The Developer Economics Q4 2018 survey is for all developers (professionals, hobbyists, and students) engaging in the following software development areas: web, mobile, desktop, backend services, IoT, AR/VR, machine learning and data science, and gaming.
What questions am I likely to be asked?
The survey asks questions related to developer skills, and experiences with dev tools, platforms, frameworks, resources, and more.
Your background and skills for demographics What’s going up and what’s going down in the software industry? Are you working on the projects you would like to work on? Where do you think development time should be invested? Which are your favorite tools and platforms?
Also, keep an eye out for some technology trivia interspersed in the survey. You may learn something
In today’s high-speed environment, being able to process massive amounts of data each millisecond is becoming a common business requirement. We are excited to be announcing that an internal Microsoft project known as Trill for processing “a trillion events per day” is now being open sourced to address this growing trend.
Here are just a few of the reasons why developers love Trill:
As a single-node engine library, any .NET application, service, or platform can easily use Trill and start processing queries. A temporal query language allows users to express complex queries over real-time and/or offline data sets. Trill’s high performance across its intended usage scenarios means users get results with incredible speed and low latency. For example, filters operate at memory bandwidth speeds up to several billions of events per second, while grouped aggregates operate at 10 to 100 million events per second. A rich history
Trill started as a research project at Microsoft Research in 2012, and since then, has been extensively described in research papers such as VLDB and the IEEE Data Engineering Bulletin. The roots of Trill’s language lie in Microsoft’s former service StreamInsight, a powerful platform allowing developers to develop and deploy complex event processing
In the natural language processing (NLP) domain, pre-trained language representations have traditionally been a key topic for a few important use cases, such as named entity recognition (Sang and Meulder, 2003), question answering (Rajpurkar et al., 2016), and syntactic parsing (McClosky et al., 2010).
The intuition for utilizing a pre-trained model is simple: A deep neural network that is trained on large corpus, say all the Wikipedia data, should have enough knowledge about the underlying relationships between different words and sentences. It should also be easily adapted to a different domain, such as medical or financial domain, with better performance than training from scratch.
Recently, a paper called “BERT: Bidirectional Encoder Representations from Transformers” was published by Devlin, et al, which achieves new state-of-the-art results on 11 NLP tasks, using the pre-trained approach mentioned above. In this technical blog post, we want to show how customers can efficiently and easily fine-tune BERT for their custom applications using Azure Machine Learning Services. We open sourced the code on GitHub.
Intuition behind BERT
The intuition behind the new language model, BERT, is simple yet powerful. Researchers believe that a large enough deep neural network model, with large enough training corpus, could capture
We’re excited to officially announce the public preview of the built-in Python images for Azure App Service on Linux, a much requested feature by our customers. Developers can get started today deploying Python Web Apps to the cloud, on a fully-managed environment running on top of the Linux operating system.
This new preview runtime adds to a list of growing stacks supported by Azure App Service on Linux, which includes also Node.js, .NET Core, PHP, Java SE, Tomcat, and Ruby. With the choice of Python 3.7, 3.6 and soon 2.7, developers can get started quickly and deploy Python applications to the cloud, including Django and Flask, and leverage the full suite of features of Azure App Service on Linux. This includes support for deployments via “git push”, and the ability to deploy and debug live applications using Visual Studio Code (our free and open source editor for macOS, Linux, and Windows).
When you use the official images for Python on App Service on Linux, the platform automatically installs the dependencies specified in the requirements.txt file. Additionally, it detects common Flask and Django application structures and hosts them using gunicorn, and includes the necessary modules for connecting to Azure DB for
Monitoring and managing the performance of your data warehouse is critical to the overall health of your data estate. With the increase in data and query velocities, tracking query metrics pertaining to usage frequency, resource consumption, or regressions can impact your ability to efficiently draw meaningful insights from your data.
To increase your efficiency, we’re excited to reveal the preview of Query Store for Azure SQL Data Warehouse for both our Gen1 and Gen2 offers. Query Store is designed to help you with query performance troubleshooting by tracking queries, query plans, runtime statistics, and query history to help you monitor the activity and performance of your data warehouse. Query Store is a set of internal stores and Dynamic Management Views (DMVs) that allow you to:
Identify and tune top resource consuming queries. Identify and improve ad hoc workloads. Evaluate query performance and impact to the plan by changes in statistics, indexes, or system size (DWU setting). See full query text for all queries executed.
The Query Store contains three actual stores: a plan store for persisting the execution plan information, a runtime stats store for persisting the execution statistics information, and a wait stats store for persisting wait stats information.