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Unlocking the promise of IoT: A Q&A with Vernon Turner

Vernon Turner is the Founder and Chief Strategist at Causeway Connections, an information and communications technology research firm. For nearly a decade, he’s been serving on global, national, and state steering committees, advising governments, businesses, and communities on IoT-based solution implementation. He recently talked with us about the importance of distinguishing between IoT hype and reality, and identifies three steps businesses need to take to make a successful digital transformation.

What is the promise of IoT?

The promise of more and more data from more and more connected sensors boils down to unprecedented insights and efficiencies. Businesses get more visibility into their operations, a better understanding of their customers, and the ability to personalize offerings and experiences like never before, as well as the ability to cut operational costs via automation and business-process efficiencies.

But just dabbling with IoT won’t unlock real business value. To do that, companies need to change everything, how they make products, how they go to market, their strategy, and their organizational structure. They need to really transform. And to do that, they need to do three things, lead with the customer experience, migrate to offering subscription-based IoT-enabled services, and have a voice in an emergent ecosystem of partners related to their business.

Why is the customer experience so important to fulfilling the promise of IoT?

There can be a lot of hype around IoT-enabled offerings. 

I recently toured several so-called smart buildings with a friend in the construction industry. He showed me that just filling a building with IoT-enabled gadgets doesn’t make it smart. A truly smart building goes beyond connected features and addresses the specific, real-world needs of tenants, leaseholders, and building managers.

If it doesn’t radically change the customer experience, it doesn’t fulfill the promise of IoT.

What’s the disconnect? Why aren’t “smart” solution vendors delivering what customers want?

Frankly, it’s easier to sell a product than an experience.

Customer experience should be at the center of the pitch for IoT, because IoT enables customers to have much more information about the product, in real-time, across the product lifecycle. But putting customer experience first requires making hard changes. It means adopting new strategies, business models, and organization charts, as well as new approaches to product development, sales and marketing, and talent management. And it means asking suppliers to create new business models to support sharing data across the product lifecycle.

Why is the second step to digital transformation, migrating to offering subscription-based, IoT-enabled services, so important?

To survive in our digitally transforming economy, it’s essential for businesses and their suppliers, to move from selling static products to a subscription-based services business model.

As sensors and other connected devices become increasingly omnipresent, customers see more real-time data showing them exactly what they’re consuming, and how the providers of the services they’re consuming are performing. By moving to a subscription (or “X as a service”) model, businesses can provide more tailored offerings, grow their customer base, and position themselves for success in the digital age.

When companies embrace transformation, it can have a ripple effect across their operations. Business units can respond to market needs to create a new service by combining microservices using the rapid software development techniques of DevOps. These services drive a shift from infrequent, low-business-value interactions with customers to continuous engagement between customers and companies’ sales and business units. This improves customer relationships, staving off competition, and introducing new sales opportunities.

What challenges should companies be prepared for as they migrate to offering subscription services?

For a subscription-based services model to work, most companies need to make significant changes to their culture and organizational structure.

Financial planning needs to stop reviewing past financial statements and start focusing on future recurring revenue. Instead of concentrating on margin-based products, sales should start selling outcomes that add value for customers. Marketing must be driven by data about the customer experience and what the customer needs, rather than what serves the branding campaign.

From now on, rapid change, responsiveness to the customer, and the ability to customize and scale services are going to be the norm in business.

You mentioned the importance of participating in an emergent ecosystem of partners. What does that mean? Why does it matter?

As digital business processes mature and subscription models become the standard, customers will demand ways to integrate their relationships with IT and business vendors in an ecosystem connected by a single platform.

Early results show that vendors who actively participate in their solution platform’s ecosystem enjoy a higher net promoter score (NPS). In the short term, they gain stickiness with customers. And in the long run, they become more relevant across their ecosystem, gain a competitive advantage over peers inside and outside their ecosystem, and deliver more value to customers.

How does ecosystem participation increase the value delivered to customers?

Because everyone’s using the same platform, customers get transparency into the performance of suppliers. Service-level management becomes the first point of contact between businesses and suppliers. Key performance indicators trigger automatic responses to customer experiences. Response times to resolve issues are mediated by the platform.

These tasks and functions are carried out within the ecosystem and orchestrated by third-party service management companies. But that’s not to say businesses in the ecosystem don’t still have an individual, separate relationship with their customers. Rather, the ecosystem acts as a gateway for IT and business suppliers to integrate their offerings into customer services. Business and product outcomes from the ecosystem feed research and development, product design, and manufacturing, leading to continual improvement in services delivery and customer experience.

To conclude, let’s go back to something we talked about earlier. For builders, a truly smart building is one that does more than just keep the right temperature. It also monitors and secures wireless networks, optimizes lighting based on tenants’ specific needs, manages energy use, and so on to deliver comfortable, customized work, living, or shopping environments. To deliver that kind of customer experience takes an ecosystem of partners, all working in concert. For companies to unlock the value of IoT, they need to participate actively in that ecosystem.

Learn how Azure helps businesses unlock the value of IoT.
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PyTorch on Azure with streamlined ML lifecycle

It's exciting to see the Pytorch Community continue to grow and regularly release updated versions of PyTorch! Recent releases improve performance, ONNX export, TorchScript, C++ frontend, JIT, and distributed training. Several new experimental features, such as quantization, have also been introduced.

At the PyTorch Developer Conference earlier this fall, we presented how our open source contributions to PyTorch make it better for everyone in the community. We also talked about how Microsoft uses PyTorch to develop machine learning models for services like Bing. Whether you are an individual, a small team, or a large enterprise, managing the machine learning lifecycle can be challenging. We'd like to show you how Azure Machine Learning can make you and your organization more productive with PyTorch.

Streamlining the research to production lifecycle with Azure Machine Learning

One of the benefits of using PyTorch 1.3 in Azure Machine Learning is Machine Learning Operations (MLOps). MLOps streamlines the end-to-end machine learning (ML) lifecycle so you can frequently update models, test new models, and continuously roll out new ML models alongside your other applications and services. MLOps provides:

Reproducible training with powerful ML pipelines that stitch together all the steps involved in training your PyTorch model, from data preparation, to feature extraction, to hyperparameter tuning, to model evaluation.
Asset tracking with dataset and model registries so you know who is publishing PyTorch models, why changes are being made, and when your PyTorch models were deployed or used in production.
Packaging, profiling, validation, and deployment of PyTorch models anywhere from the cloud to the edge.
Monitoring and management of your PyTorch models at scale in an enterprise-ready fashion with eventing and notification of business impacting issues like data drift.

 

Training PyTorch Models

With MLOps, data scientists write and update their code as usual and regularly push it to a GitHub repository. This triggers an Azure DevOps build pipeline that performs code quality checks, data sanity tests, unit tests, builds an Azure Machine Learning pipeline, and publishes it to your Azure Machine Learning workspace.

The Azure Machine Learning pipeline does the following tasks:

Train model task executes the PyTorch training script on Azure Machine Learning compute. It outputs a model file which is stored in the run history.
Evaluate model task evaluates the performance of the newly trained PyTorch model with the model in production. If the new model performs better than the production model, the following steps are executed. If not, they will be skipped.
Register model task takes the improved PyTorch model and registers it with the Azure Machine Learning model registry. This allows us to version control it.

You can find example code for training a PyTorch model, doing hyperparameter sweeps, and registering the model in this PyTorch MLOps example.

Deploying PyTorch models

The Machine Learning extension for DevOps helps you integrate Azure Machine Learning tasks in your Azure DevOps project to simplify and automate model deployments. Once a new model is registered in your Azure Machine Learning workspace, you can trigger a release pipeline to automate your deployment process. Models can then be automatically packaged and deployed as a web service across test and production environments such as Azure Container Instances and Azure Kubernetes Service (AKS). You can even enable gated releases so that, once the model is successfully deployed to the staging or quality assurance (QA) environment, a notification is sent to approvers to review and approve the release to production. You can see sample code for this in the PyTorch ML Ops example.

Next steps

We’re excited to support the latest version of PyTorch in Azure. With Azure Machine Learning and its MLOps capabilities, you can use PyTorch in your enterprise with a reproducible model lifecycle. Check out the MLOps example repository for an end to end example of how to enable a CI/CD workflow for PyTorch models.
Quelle: Azure

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Bing delivers its largest improvement in search experience using Azure GPUs

Over the last couple of years, deep learning has become widely adopted across the Bing search stack and powers a vast number of our intelligent features. We use natural language models to improve our core search algorithm’s understanding of a user’s search intent and the related webpages so that Bing can deliver the most relevant search results to our users. We rely on deep learning computer vision techniques to enhance the discoverability of billions of images even if they don’t have accompanying text descriptions or summary metadata. We leverage machine-based reading comprehension models to retrieve captions within larger text bodies that directly answer the specific questions users have. All these enhancements lead toward more relevant, contextual results for web search queries.

Recently, there was a breakthrough in natural language understanding with a type of model called transformers (as popularized by Bidirectional Encoder Representations from Transformers, BERT). Unlike previous deep neural network (DNN) architectures that processed words individually in order, transformers understand the context and relationship between each word and all the words around it in a sentence. Starting from April of this year, we used large transformer models to deliver the largest quality improvements to our Bing customers in the past year. For example, in the query "what can aggravate a concussion", the word "aggravate" indicates the user wants to learn about actions to be taken after a concussion and not about causes or symptoms. Our search powered by these models can now understand the user intent and deliver a more useful result. More importantly, these models are now applied to every Bing search query globally making Bing results more relevant and intelligent.

Deep learning at web-search scale can be prohibitively expensive

Bing customers expect an extremely fast search experience and every millisecond of latency matters.  Transformer-based models are pre-trained with up to billions of parameters, which is a sizable increase in parameter size and computation requirement as compared to previous network architectures. A distilled three-layer BERT model serving latency on twenty CPU cores was initially benchmarked at 77ms per inference. However, since these models would need to run over millions of different queries and snippets per second to power web search, even 77ms per inference remained prohibitively expensive at web search scale, requiring tens of thousands of servers to ship just one search improvement.

Leveraging Azure Virtual Machine GPUs to achieve 800x inference throughput

One of the major differences between transformers and previous DNN architectures is that it relies on massive parallel compute instead of sequential processing. Given that graphics processing unit (GPU) architecture was designed for high throughput parallel computing, Azure’s N-series Virtual Machines (VM) with GPU accelerators built-in were a natural fit to accelerate these transformer models. We decided to start with the NV6 Virtual Machine primarily because of the lower cost and regional availability.  Just by running the three-layer BERT model on that VM with GPU, we observed a serving latency of 20ms (about 3x improvement). To further improve the serving efficiency, we partnered with NVIDIA to take full advantage of the GPU architecture and re-implemented the entire model using TensorRT C++ APIs and CUDA or CUBLAS libraries, including rewriting the embedding, transformer, and output layers.  NVIDIA also contributed efficient CUDA transformer plugins including softmax, GELU, normalization, and reduction.

We benchmarked the TensorRT-optimized GPU model on the same Azure NV6 Virtual Machine and was able to serve a batch of five inferences in 9ms, an 8x latency speedup and 43x throughput improvement compared to the model without GPU acceleration. We then leveraged Tensor Cores with mixed precision on a NC6s_v3 Virtual Machine to even further optimize the performance, benchmarking a batch size of 64 inferences at 6ms (~800x throughput improvement compared to CPU).

Transforming the Bing search experience worldwide using Azure’s global scale

With these GPU optimizations, we were able to use 2000+ Azure GPU Virtual Machines across four regions to serve over 1 million BERT inferences per second worldwide. Azure N-series GPU VMs are critical in enabling transformative AI workloads and product quality improvements for Bing with high availability, agility, and significant cost savings, especially as deep learning models continue to grow in complexity. Our takeaway was very clear, even large organizations like Bing can accelerate their AI workloads by using N-series virtual machines on Azure with built-in GPU acceleration. Delivering this kind of global-scale AI inferencing without GPUs would have required an exponentially higher number of CPU-based VMs, which ultimately would have become cost-prohibitive.  Azure also provides customers with the agility to deploy across multiple types of GPUs immediately, which would have taken months of time if we were to install GPUs on-premises.  The N-series Virtual Machines were essential to our ability to optimize and ship advanced deep learning models to improve Bing search, available globally today.

N-series general availability

Azure provides a full portfolio of Virtual Machine capabilities across the NC, ND, and NV series product lines. These Virtual Machines are designed for application scenarios for which GPU acceleration is common, such as compute-intensive, graphics-intensive, and visualization workloads.

NC-series Virtual Machines are optimized for compute-intensive and network-intensive applications.
ND-series Virtual Machines are optimized for training and inferencing scenarios for deep learning.
NV-series Virtual Machines are optimized for visualization, streaming, gaming, encoding, and VDI scenarios.

See our Supercomputing19 blog for recent product additions to the ND and NV-series Virtual Machines.

Learn more

Join us at Supercomputing19 to learn more about our Bing optimization journey, leveraging Azure GPUs.
Quelle: Azure

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Helping Smart Cities become more Inclusive

According to the UN, we will see the world's urban populations grow from today's 55 percent to 68 percent by 2050. With almost a billion people on the path to be urban dwellers, most cities are still unfriendly to people with disabilities. As more people flock to cities, making our cities smarter and more inclusive will become increasingly important. The concept of smart cities is all about developing strategies that leverage data and technology to enhance urban life. The IoT plays a central role in collecting sensor data and then using the insights gained from that data to manage assets, resources, and services efficiently.

As city planners tackle the complex challenges of increasing urbanization, managing scarce resources, climate change, and creating safer more accessible cities, Azure Maps (a collection of geospatial APIs) becomes a critical tool for city planners. A key aspect of IoT & technology solutions is that they should be intuitive, easy to use, and accessible.

Azure Maps and accessibility

Azure Maps makes it easy for all users to navigate an interactive map experience. Users can interact with maps using a mouse, touch, or keyboard. Azure Maps provides screen readers with enhanced descriptions that can combine multiple updates into a single message that is easier to digest and understand. Recently Azure Maps achieved exciting new capabilities & Microsoft certification around accessibility. All apps, both Microsoft owned and third party, that use Azure Maps will benefit from the accessibility features that are provided out of the box.

Azure Maps also relies on best of breed content partnerships for everything from the maps data, traffic, real time transit, ride share, to weather data.

Moovit helps people with disabilities ride transit

One of the Azure Maps content partnerships is with Moovit. Launched in 2011 in Israel, Moovit has become the world’s most popular transit-planning and navigation app, with more than 500 million users and service in over 3,000 cities across 94 countries. The company is also a leader in inclusive technology, with innovative work that helps people across the disability spectrum use buses, trains, subways, ride-hailing services, and other modes of public transit.

In addition to offering a consumer app in 45 languages, Moovit has partnered with Microsoft to provide its multi-modal transit data to developers who use Azure Maps, and a set of mobility-as-a-service solutions to cities, governments, and organizations. The partnership will enable the creation of more inclusive smart cities and more accessible transit apps.

How Aira helps smart cities become more accessible

One of the companies that is leveraging the geospatial and mapping capabilities from Azure Maps and the transit capabilities from Moovit, is Aira. Aira is a technology company dedicated to making lives simpler and more engaging. Based in San Diego, California, they build solutions to connect people who are blind, have low vision, or are simply aging into a digital world, with highly-trained professionals who provide visual information on demand.

Public transportation is the lifeline to jobs, education, healthcare, and more, yet many blind or low vision riders still have trouble getting to their destination. They may be uncertain that they’ve caught the right bus, or unable to read the entrance sign they need to follow in order to access the subway. In addition, as populations age, the number of people experiencing age-related vision loss rises every day. Moovit, Microsoft, and Aira are joining forces in order to challenge these obstacles and make public transit more accessible and inclusive, empowering blind and low vision riders to travel with more confidence.

“In Azure Maps, we invested significant time and resources to define accessibility requirements, implementing capabilities for those with needs, and pushing ourselves to service this segment of users” says Chris Pendleton, Head of Azure Maps at Microsoft Corp. “I’m elated to see Aira, Moovit, and Azure Maps providing services together further justifying our investments in the benefit of those who need it most”

Smart City Expo World Congress

In order to connect with cities on their journeys for digital transformation, the Azure Maps team, along with Moovit and Aira, will be at Smart City Expo World Congress, the industry-leading event for urbanization, showcasing technologies and partners enabling the digital transformation of smart cities. For updates from SCEWC, follow us on twitter.    

 
Quelle: Azure

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Accelerate IoMT on FHIR with new Microsoft OSS Connector

Microsoft is expanding the ecosystem of FHIR® for developers with a new tool to securely ingest, normalize, and persist Protected Health Information (PHI) from IoMT devices in the cloud.  

Continuing our commitment to remove the barriers of interoperability in healthcare, we are excited to expand our portfolio of Open Source Software (OSS) to support the HL7 FHIR Standard (Fast Healthcare Interoperability Resource). The release of the new IoMT FHIR Connector for Azure is available today in GitHub.

The Internet of Medical Things (IoMT) is the subset of IoT devices that capture and transmit patient health data. It represents one of the largest technology revolutions changing the way we deliver healthcare, but IoMT also presents a big challenge for data management.

Data from IoMT devices is often high frequency, high volume, and requires sub-second measurements. Developers have to deal with a range of devices and schemas, from sensors worn on the body, ambient data capture devices, applications that document patient reported outcomes, and even devices that only require the patient to be within a few meters of a sensor.

Traditional healthcare providers, innovators, and even pharma and life sciences researchers are ushering in a new era of healthcare that leverages machine learning and analytics from IoMT devices. Most see a future where devices monitoring patients in their daily lives will be used as a standard approach to deliver cost savings, improve patient visibility outside of the physician’s office, and to create new insights for patient care. Yet as new IoMT apps and solutions are developed, two consistent barriers are preventing broad scalability of these solutions: interoperability of IoMT device data with the rest of the healthcare data, such as clinical or pharmaceutical records, and the security and private exchange of protected health information (PHI) from these devices in the cloud.

In the last several years, the provider ecosystem began to embrace the open source standard of FHIR as a solution for interoperability. FHIR is rapidly becoming the preferred standard for exchanging and managing healthcare information in electronic format and has been most successful in the exchange of clinical health records. We wanted to expand the ecosystem and help developers working with IoMT devices to normalize their data output in FHIR. The robust, extensible data model of FHIR standardizes the semantics of healthcare data and defines standards for exchange, so it fuels interoperability across data systems. We imagined a world where data from multiple device inputs and clinical health data sets could be quickly normalized around FHIR and work together in just minutes, without the added cost and engineering work to manage custom configurations and integration with each and every device and app interface. We wanted to deliver foundational technology that developers could trust so they could focus on innovation. And today, we’re releasing the IoMT FHIR Connector for Azure.

This OSS release opens an exciting new horizon for healthcare data management. It provides a simple tool that can empower application developers and technical professionals working with data from devices to quickly ingest and transform that data into FHIR. By connecting to the Azure API for FHIR, developers can set up a robust and secure pipeline to manage data from IoMT devices in the cloud.

The IoMT FHIR Connector for Azure enables easy deployment in minutes, so developers can begin managing IoMT data in a FHIR Server that supports the latest R4 version of FHIR:

Rapid provisioning for ingestion of IoMT data and connectivity to a designated FHIR Server for secure, private, and compliant persistence of PHI data in the cloud
Normalization and integrated mapping to transform data to the HL7 FHIR R4 Standard
Seamless connectivity with Azure Stream Analytics to query and refine IoMT data in real-time
Simplified IoMT device management and the ability to scale through Azure IoT services (including Azure IoT Hub or Azure IoT Central)
Secure management for PHI data in the cloud, the IoMT FHIR Connector for Azure has been developed for HIPAA, HITRUST, and GDPR compliance and in full support of requirements for protected health information (PHI)

To enhance scale and connectivity with common patient-facing platforms that collect device data, we’ve also created a FHIR HealthKit framework that works with the IoMT FHIR Connector. If patients are managing data from multiple devices through the Apple Health application, a developer can use the IoMT FHIR Connector to quickly ingest data from all of the devices through the HealthKit API and export it to their FHIR server.

Playing with FHIR
The Microsoft Health engineering team is fully backing this open source project, but like all open source, we are excited to see it grow and improve based on the community's feedback and contributions. Next week we’ll be joining developers around the world for FHIR Dev Days in Amsterdam to play with the new IoMT FHIR Connector for Azure. Learn more about the architecture of the IoMT FHIR Connector and how to contribute to the project on our GitHub page.

FHIR® is the registered trademark of HL7 and is used with the permission of HL7
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Forrester names Microsoft a leader in Wave report for Industrial IoT Software Platforms

As a company, we work every day to empower every person on the planet to achieve more. As part of that, we’re committed to investing in IoT and intelligent edge, two technology trends accelerating ubiquitous computing and bringing unparalleled opportunity for transformation across industries. We’ve been working hard to make our Azure IoT platform more open, security-enhanced, and scalable, as well as to create opportunities in new market areas and our growing partner ecosystem. Our core focus is addressing the industry challenge of securing connected devices at every layer and advancing IoT to create a more seamless experience between the physical and digital worlds.

Today, Microsoft is positioned as a leader in The Forrester Wave™: Industrial IoT Software Platforms, Q4 2019, receiving of the highest score possible, 5.00, in partner strategy, innovation roadmap, and platform differentiation criteria, the highest score in the market presence category, and the second-highest score in the current offering category.

According to the Forrester report, “Microsoft powers industrial partners but also delivers a credible platform of its own. Microsoft continues to add features to the platform at an impressive rate, with the richer edge capabilities of Azure IoT Edge and the simplified application and device onboarding offered by Azure IoT Central formally launching since we last evaluated this market.”

We believe this latest recognition spotlights our commitment and ability to:

Support a comprehensive set of deployment models, from edge to cloud. According to our own IoT Signals research, the decision-makers surveyed believe that in the next two years, AI, edge computing, and 5G will be critical technological drivers for IoT success. And they want tools that can drive success across diverse deployment models.

Deliver business integration that goes beyond connectivity and device management. It’s become increasingly important for businesses to be able to link IoT workflows to data and processes across the operation, and we’re helping customers accelerate time to value.

Turn analytics into actionable intelligence. Industrial firms capture and generate mountains of time-series data in real-time. Transforming this data into timely insights is key to turning that data into decisions that move the business forward.

We’re committed to making Azure the ideal IoT platform, and this recognition comes at a great point in our journey. Download this complimentary full report and read the analysis behind Microsoft’s positioning as a Leader.

More information on our Azure IoT Industrial platform.

The Forrester Wave™: Industrial IoT Software Platforms, Q4 2019, Michele Pelino and Paul Miller, November 13, 2019. This graphic was published by Forrester Research as part of a larger research document and should be evaluated in the context of the entire document. 

Quelle: Azure

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How to build globally distributed applications with Azure Cosmos DB and Pulumi

This post was co-authored by Mikhail Shilkov, Software Engineer, Pulumi.

Pulumi is reinventing how people build modern cloud applications, with a unique platform that combines deep systems and infrastructure innovation with elegant programming models and developer tools.

We live in amazing times when people and businesses on different continents can interact at the speed of light. Numerous industries and applications target users around the globe: e-commerce websites, multiplayer online games, connected IoT devices, collaborative work and leisure experiences, and many more. All of these applications demand computing and data infrastructure in proximity to the end-customers to minimize latency and keep the user experience engaging. The modern cloud makes these scenarios possible. 

Azure infrastructure

Azure Cosmos DB provides a turn-key data distribution to any number of regions, meaning that locations can be added or removed along the way while running production workloads. Azure takes care of data replication, resiliency, and efficiency while providing APIs for read and write operations with a latency of less than 10 milliseconds.

In contrast, compute services—virtual machines, container instances, Azure App Services, Azure Functions, and managed Azure Kubernetes Service—are located in a single Azure region. To make good use of the geographic redundancy of the database, users should deploy their application to each of the target regions.

 

Globally distributed application

Application regions must stay in sync with Azure Cosmos DB regions to enjoy low-latency benefits. Operational teams must manage the pool of applications and services to provide the correct locality in addition to auto-scaling configuration, efficient networking, security, and maintainability.

To help manage the complexity, the approach of infrastructure as code comes to the rescue.

Infrastructure as code

While the Azure portal is an excellent pane-of-glass for all Azure services, it shouldn’t be used directly to provision production applications. Instead, we should strive to describe the infrastructure in terms of a program which can be executed to create all the required cloud resources.

Traditionally, this could be achieved with an automation script, e.g., a PowerShell Cmdlet or a bash script calling the Azure CLI. However, this approach is laborious and error prone. Bringing an environment from its current state to the desired is often non-trivial. A failure in the middle of the script often requires manual intervention to repair environments, leading to downtime.

Desired state configuration is another style of infrastructure definition. A user describes the desired final state of infrastructure in a declarative manner, and the tooling takes care of bringing an environment from its current state to the parity with the desired state. Such a program is more natural to evolve and track changes.

Azure Resource Manager Templates is the bespoke desired-state-configuration tool in the world of Azure. The state is described as a JSON template, listing all the resources and properties. However, large JSON templates can be quite hard to write manually. They have a high learning curve and quickly become large, complex, verbose, and repetitive. Developers find themselves missing simple programming language possibilities like iterations or custom functions.

Pulumi solves this problem by using general-purpose programming languages to describe the desired state of cloud infrastructure. Using JavaScript, TypeScript, or Python reduces the amount of code many-fold, while bringing constructs like functions and components to the DevOps toolbox.

Global applications with Pulumi

To illustrate the point, we develpoed a TypeScript program to provision a distributed application in Azure.

The target scenario requires quite a few resources to distribute the application across multiple Azure regions, including:

Provision an Azure Cosmos DB account in multiple regions
Deploy a copy of the application layer to each of those regions
Connect each application to the Azure Cosmos DB local replica
Add a Traffic Manager to route user requests to the nearest application endpoint

Global application with Azure and Pulumi

 

However, instead of coding this manually, we can rely on Pulumi's CosmosApp component as described in How To Build Globally Distributed Applications with Azure Cosmos DB and Pulumi. The component creates distributed Azure Cosmos DB resources, as well as the front-end routing component while allowing pluggable compute layer implementation.

You can find the sample code in Reusable Component to Create Globally-distributed Applications with Azure Cosmos DB.

Pulumi CLI executes the code, translate it to the tree of resources to create, and deploys all of them to Azure:

After the command succeeds, the application is up and running in three regions of my choice.

Next steps

Infrastructure as code is instrumental in enabling modern DevOps practices in the universe of global and scalable cloud applications.

Pulumi lets you use a general-purpose programming language to define infrastructure. It brings the best tools and practices from the software development world to the domain of infrastructure management.

Try the CosmosApp (available on GitHub—TypeScript, C#) with serverless functions, containers, or virtual machines to get started with Pulumi and Azure.
Quelle: Azure

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Democratizing Smart City solutions with Azure IoT Central

One of the most dynamic landscapes embracing Internet of Things (IoT) is the modern city. As urbanization grows, city leaders are under increasing pressure to make cities safer, accessible, sustainable, and prosperous.

Underlying all these important goals is the bedrock that makes a city run: infrastructure. Whether it be water, electricity, streets, traffic lights, cities are increasingly using the Internet of Things (IoT) to manage their infrastructure by capturing and analyzing data from connected devices and sensors. This gives city managers real-time insights to improve operational efficiency and outcomes and to altogether rethink and reinvent city government functions and operations.

Microsoft and its ecosystem of service and hardware providers are deeply engaged with cities and communities around the world, addressing the most pressing issues that government leaders face. For instance, traffic congestion continues to increase in most urban areas, placing growing pressure on existing physical infrastructure. In the emerging world, new physical infrastructure needs to be built altogether. Citizens also have growing concerns about public safety and security. Investments in IoT-based solutions for city operations are accelerating to address these concerns, led by applications like smart street lighting, smart waste, and smart parking. Cities are also realizing the benefit of IoT for optimizing the management of globally scarce resources, such as water and energy. Amidst this growing investment, early results from the world's leading smart cities are promising. Some cities have seen approximately 60 percent in energy savings from leveraging LED-based smart streetlights, while others have been able to save 25-80 liters of water per person per day. Optimized traffic flow in some areas is helping commuters shave 15-30 minutes daily, resulting in a 10-15 percent reduction in emissions, and 66 percent operational cost reduction from smart waste management.

Despite a growing consensus around the benefits of adopting IoT solutions, scaling beyond the proof of concept remains difficult. Most smart city solutions today consist of bespoke pilots, unable to scale or repeat due to growing costs, complexity, and lack of specialized technical talent, in a market landscape that is already incredibly fragmented. Earlier this year we surveyed 3,000 enterprise decision-makers across the world, including government organizations, of whom 83 percent consider IoT “critical” to success, notably for public safety and infrastructure and facilities management. At the same time, the vast majority of the decision-makers expressed concerns about persistent knowledge gaps for how to scale their solutions securely, reliably, and affordably, the main reason why the average maturity of production-level IoT projects remains extremely low (read the full IoT Signals report). In order to help IoT solution builders navigate the complexity of designing enterprise-grade IoT systems, we published our learnings in a whitepaper called “The 8 attributes of successful IoT solutions” to help IoT solution builders ask the right questions up front as they design their systems, and to help them select the right technology platforms.

Building Smart Cities IoT solutions with Azure IoT Central

To further help IoT solution builders confidently scale their projects, we recently announced updates to Azure IoT Central, our IoT app platform for designing, deploying, and managing enterprise-grade solutions. IoT Central provides a fully managed platform for building and customizing solutions, designed to support solution builders with each of the attributes of successful IoT systems, including security, disaster recovery, high availability, and more. By removing the complexity and overhead of setup, management, and operations, IoT Central is lowering the barrier for IoT solution builders across industries, and accelerates the creation of innovative solutions across all industries, from retail to healthcare to energy to government. Check out our recent IoT Central blog for a full list of our updates and examples of solution builders across different industries.

As part of our mission to democratize IoT for all, we released an initial set of Azure IoT Central government app templates to help solution builders start building IoT solutions quickly with out-of-box device command and control, monitoring and alerting, a user interface with built-in permissions, configurable dashboards, and extensibility APIs. Solution builders can brand, customize, and easily connect their solutions to their line of business applications, such as Dynamics 365 for integrated field service, Azure ML services, or their third-party services of choice.

Developers can get started today with any of the government app templates for free and access starter resources, including sample operator dashboards, simulated devices, pre-configured rules, and alerting to explore what is possible. We’ve also provided guidance for customizing and extending solutions with documentation, tutorials, and how-to’s. Ultimately you can brand and sell your finished solution to your customers, either directly or through Microsoft AppSource.

Government app templates available today:

Connected waste management: Sensors deployed in garbage containers in cities can inform how full a trash bin is and optimize waste collection routes. Moreover, advanced capabilities for smart waste applications involve the use of analytics to detect bin contamination.

Water quality monitoring: Traditional water quality monitoring relies on manual sampling techniques and field laboratory analysis, which is both time consuming and costly. By remotely monitoring water quality in real-time, water quality issues can be managed before citizens are impacted.

Water consumption monitoring: Traditional water consumption tracking relies on water operators manually reading water meters across various sites. More and more cities are replacing traditional meters with advanced smart meters, enabling remote monitoring of consumption as well as remote control of valves to manage water flow. Water consumption monitoring coupled with information and insights flowing back to individual households can increase awareness and reduce water consumption.

Expect to see more app templates for solution builders over time to cover other smart city scenarios, with templates for smart streetlights, air quality monitoring, smart parking, and more.

Innovative smart cities solution partners using Azure IoT Central

From established leading research organizations to enterprises to public utilities, we are seeing solution builders leverage Azure IoT Central to transform their public sector services.

Smart water infrastructure

Dutch-based company, Oasen, supplies 48 billion liters of high-quality drinking water every year to 750,000 residents across municipalities in the South Holland region. Oasen turned to Microsoft and OrangeNXT to digitally transform its water structure. Using Azure IoT Central, the company is introducing scalability, flexibility, and greater innovation to its operations through remote management of its water distribution network. Leveraging Azure Digital Twins and Azure IoT Central, Oasen connects multiple sources of data (including data extracted from smart water meters and smart valves in pipelines), to create a true digital twin of the water grid.

By remotely controlling and monitoring valves, Oasen can now automatically test grid sections (step-testing) to radically improve grid quality, as well as predict burst water mains and assess which pipelines are most at risk of damage and need repair. These smart water shutters and smart meter implementations significantly reduce manual work. Furthermore, the smart grid solution allows the automatic shutdown of sections of the distribution network if a leak is detected, preventing damage, and reducing water quality hazards.

Water quality monitoring

Other solution builders have built solutions for water quality management. According to the World Health Organization, nearly one-fourth of people across the globe drink water contaminated with feces, with an estimated 50 percent of the global population projected to live in water-stressed areas by 2025, (either in close proximity to polluted or otherwise scarce water sources). There has never been a greater need for high-quality data from liquid sensor networks to track ion levels in the water, which can fluctuate dramatically within the scope of several hundred meters and can have devastating impacts on public health. Imec, a leading international research and development firm specializing in nanoelectronics and digital technology, has developed water sensor devices from inexpensive ion sensors on silicone substrates for monitoring water quality in real-time.

Imec, together with partners, will pilot this solution in a testbed of about 2,500 sensors installed across the Flanders region in Belgium. The sensors detect salinity in the water in real-time, allowing officials to track water quality fluctuations over time. Imec’s water quality monitoring solution was built on Azure IoT Central, which provides the flexible foundation required to design, test, and scale the solution across the city.

“IoT Central is a fast and easy to use platform suitable for an innovative R&D organization such as ours. This means we can dedicate ourselves to enable large fine-grained networks of water quality sensors and, through the collected data, improve visibility into water quality and enable better water management to the mission to make water quality better visible. ”—Marcel Zevenbergen, Program Manager, Imec

Smart street lighting

Combined with LED conversion, smart street lighting solutions have helped uncover massive efficiency opportunities for cities, with operational savings typically reaching over 65 percent. Telensa is a world leader in connected streetlight solutions, managing over 1.7 million poles in 400 cities around the world. Telensa PLANet is an end-to-end smart street lighting system consisting of wireless nodes that connect individual lights to a dedicated network and a central management application. The system helps cities reduce energy and maintenance costs while improving the efficiency of maintenance through automatic fault reporting and turning streetlight poles into hubs for other smart city sensors, such as for air quality and traffic monitoring. Since no two cities are the same, Telensa has developed its Urban IQ solution to enables cities to add any 3rd party sensors to their connected street lighting, make the insights available across city departments, and to provide sophisticated real-time visualization out of the box. Telensa built its Urban IQ solution with Azure IoT Central, to fit with current systems and to be ready for future directions. By moving device management and connectivity functions to IoT Central,  and dramatically lowering the cost of adding other sense and control apps to their Azure data fabric, Telensa can focus on enhancing smart city functionality and adding value for its customers.

Connecting the dots for smarter cities

With solutions that take full advantage of the intelligent cloud and intelligent edge, we continue to demonstrate how cloud, IoT, and artificial intelligence (AI) have the power to drastically transform and enhances cities to be more sustainable, enjoyable, and inclusive. Azure IoT continues to accelerate results with a growing and diverse set of partners creating solutions relevant to smart cities from spatially-aware solutions that provide real-world context, to smart grids of the future, to urban mobility and spatial intelligence. Together, we can build more intelligent and connected cities that empower people and organizations to achieve more.

Get started today with Azure IoT Central.

Smart City Expo World Congress

Microsoft will be at Smart City Expo World Congress, the industry-leading event for urbanization, to connect smart city technologies and partners with cities on a digital transformation journey. Visit our booth at Gran Via, Hall P2, Stand B223 and learn more about our conference presence at SCEWC 2019. We also encourage you to meet with us at the following sessions:

Congress | Solutions Talk: Keys to Achieving Digital Transformation in Government – Wednesday, November 20 at 10:30 AM
Microsoft Booth 33 – Learning Hub: Talking cities: from smart streetlights to smart water to smart traffic – Wednesday, November 20 at 15:00 PM
Microsoft Booth 33 – Learning Hub: Mobility insights for Smart City AI – Tuesday, November 19th at 12:00 PM

Quelle: Azure

da Agency

Azure Container Registry: Preview of diagnostic and audit logs

The Azure Container Registry team is happy to announce the preview of audit logs – one of our top items on UserVoice. In this release, we have new Azure portal and command-line interface (CLI) experiences to enable resource logs for diagnostic and audit evaluation of your registry logs.

This feature enables a capability to monitor your container registry by providing an audit trail of all relevant user driven activities on the registry. These logs contain information related to authentication, login details, repository level activities, and other user-driven events. In addition to these logs, Azure also provides a generic activity log which maintains a range of Azure Resource Manager information, including service health and other Azure management operations on the registry.

This feature also enables a user to turn on the resource logs for their container registry and can help facilitate with some of their compliance and diagnosing needs related to:

Security and compliance related tracking.
Diagnosing operational issues related to registry activities such as pull, push events.

Collection of resource logs for your registry however requires some additional steps as they are not turned on by default. Figure one displays how to configure diagnostics settings to enable Log Analytics. The logs can be viewed in Azure Monitor but would first require to be collected into a Log Analytics workspace.

Figure one

You can find the detailed steps to set up diagnostic workspace for collecting the logs and to use Azure Monitor for viewing the registry logs.

Azure Monitor is the consistent means to view and visualize your resource logs in Azure. Once the logs collections has been setup in Log Analytics, you can begin to view the logs data by running these queries. Figure 2 shows an example of running one of the sample queries.

Figure two

The current release is preview, in the future we will provide logs on other registry events like Delete, Untag, Replication, and more. Please continue to provide your feedback to help prioritize these feature asks.

Availability and feedback

Push, Pull, and Login event logs are currently available with delete and untag event logs to follow shortly.  As always, we love to hear your feedback on existing features as well as ideas for product roadmap.

Here’s a list of resources how you can use to engage with our team and provide feedback:

Roadmap – For visibility into our planned work.
UserVoice – To vote for existing requests or create a new request.
Issues – To view existing bugs and issues, logging new ones.
Azure Container Registry documents – For Container Registry tutorials and documentation.

Quelle: Azure