Kategorie: Microsoft Azure

Assess your servers with a CSV import into Azure Migrate

At Microsoft Ignite, we announced new Azure Migrate assessment capabilities that further simplify migration planning. In this post, we will demonstrate how to import servers into Azure Migrate Server Assessment through a CSV upload. Virtual servers of any hypervisor or cloud as well as physical servers can be assessed. You can get started with the CSV import feature by creating an Azure Migrate project or using your existing project.

Previously, Server Assessment required setting up an appliance in customer premises to perform discovery of VMware, Hyper-V virtual machines (VMs), and physical servers. We now also support importing and assessing servers without deploying an appliance. Import-based assessments provide support for Server Assessment features like Azure suitability analysis, migration cost planning, and performance-based rightsizing. The import-based assessment is helpful in the initial stages of migration planning, when you may not be able to deploy the appliance due to pending organizational or security constraints that prevent you from sending data to Azure.

Importing your servers is easy. Simply upload the server inventory in a CSV file as per the template provided by Azure Migrate. Only four data points are mandatory — server name, number of cores, size of memory, and operating system name. While you can run the assessment with this minimal information, we recommend you provide disk data as well to avail disk sizing in assessments.

Azure suitability analysis

The assessment determines whether a given server can be migrated as-is to Azure. Azure support is checked for each server discovered; if it is found that a server is not ready to be migrated, remediation guidance is automatically provided. You can customize your assessment by changing its properties, and regenerate the assessment reports. You can also generate an assessment report by choosing a VM series of your choice and specify the uptime of the workloads you will run in Azure.

Cost estimation and sizing

Assessment reports provide detailed cost estimates. You can optimize on cost using performance-based rightsizing assessments; the performance utilization value you specify of your on-premises server is taken into consideration to recommend an appropriate Azure Virtual Machine and disk SKU. This helps to optimize and right-size on cost as you migrate servers that might be over-provisioned in your on-premises data center. You can apply subscription offers and Reserved Instance pricing on the cost estimates

Assess your imported servers in four simple steps

Create an Azure Migrate project and add the Server Assessment solution to the project. If you already have a project, you do not need to create a new one. Download the CSV template for importing servers.
Gather the inventory data from a configuration management database (CMDB), or from your vCenter server, or Hyper-V environments. Convert the data into the format of the Azure Migrate CSV template.
Import the servers into Azure Migrate by uploading the server inventory in a CSV file as per the template.
Once you have successfully imported the servers, create assessments and review the assessment reports.

When you are ready to deploy an appliance, you can leverage the performance history gathered by the appliance for more accurate sizing, as well as plan migration phases using dependency analysis.

Get started right away by creating an Azure Migrate project. Note that the inventory metadata uploaded is persisted in the geography you select while creating the project. You can select a geography of your choice. Server Assessment is available today in Asia Pacific, Australia, Brazil, Canada, Europe, France, India, Japan, Korea, United Kingdom, and United States geographies.

In the upcoming blog, we will talk about application discovery and agentless dependency analysis.

Resources to get started

Read this tutorial on how to import and assess servers using Azure Migrate Server Assessment.
Read these tutorials on how to assess Hyper-V, VMware, or any physical or virtual servers using the appliance in Server Assessment.

Quelle: Azure

Six things to consider when using Video Indexer at scale

Your large archive of videos to index is ever-expanding, thus you have been evaluating Microsoft Video Indexer and decided that you want to take your relationship with it to the next level by scaling up.
In general, scaling shouldn’t be difficult, but when you first face such process you might not be sure what is the best way to do it. Questions like “are there any technological constraints I need to take into account?”, “Is there a smart and efficient way of doing it?”, and “can I prevent spending excess money in the process?” can cross your mind. So, here are six best practices of how to use Video Indexer at scale.

1. When uploading videos, prefer URL over sending the file as a byte array

Video Indexer does give you the choice to upload videos from URL or directly by sending the file as a byte array, but remember that the latter comes with some constraints.

First, it has file size limitations. The size of the byte array file is limited to 2 GB compared to the 30 GB upload size limitation while using URL.

Second and more importantly for your scaling, sending files using multi-part means high dependency on your network, service reliability, connectivity, upload speed, and lost packets somewhere in the world wide web, are just some of the issues that can affect your performance and hence your ability to scale. 

When you upload videos using URL you just need to give us a path to the location of a media file and we will take care of the rest (see below the field from the upload-video API).

To upload videos using URL via API you can check this short-code sample or you can use AzCopy for a fast and reliable way to get your content to a storage account from which you can submit it to Video Indexer using SAS URL.

2. Increase media reserved units if needed

Usually in the proof of concept stage when you just start using Video Indexer, you don’t need a lot of computing power. Now, when you want to scale up your usage of Video Indexer you have a larger archive of videos you want to index and you want the process to be at a pace that fits your use case. Therefore, you should think about increasing the number of compute resources you use if the current amount of computing power is just not enough.

In Azure Media Services, when talking about computing power and parallelization we talk about media reserved units (RUs), those are the compute units that determine the parameters for your media processing tasks. The number of RUs affects the number of media tasks that can be processed concurrently in each account and their type determines the speed of processing and one video might require more than one RU if its indexing is complex. When your RUs are busy, new tasks will be held in a queue until another resource is available.

We know you want to operate efficiently and you don’t want to have resources that eventually will stay idle part of the time. For that reason, we offer an auto-scale system that spins RUs down when less processing is needed and spin RUs up when you are in your rush hours (up to fully use all of your RUs). You can easily enable this functionality by turning on the autoscale in the account settings or using Update-Paid-Account-Azure-Media-Services API.

To minimize indexing duration and low throughput we recommend you start with 10 RUs of type S3. Later if you scale up to support more content or higher concurrency, and you need more resources to do so, you can contact us using the support system (on paid accounts only) to ask for more RUs allocation.

3. Respect throttling

Video Indexer is built to deal with indexing at scale, and when you want to get the most out of it you should also be aware of the system’s capabilities and design your integration accordingly. You don’t want to send an upload request for a batch of videos just to discover that some of the movies didn’t upload and you are receiving an HTTP 429 response code (too many requests). It can happen due to the fact that you sent more requests than the limit of movies per minute we support. Don’t worry, in the HTTP response, we add a retry-after header. The header we will specify when you should attempt your next retry. Make sure you respect it before trying your next request.

4. Use callback URL

Have you ever called customer service and their response was “I’m now processing your request, it will take a few minutes. You can leave your phone number and we’ll get back to you when it is done”? The cases when you do leave your number and they call you back the second your request was processed are exactly the same concept as using callback URL.

Thus we recommend that instead of polling the status of your request constantly from the second you sent the upload request, you can just add a callback URL, and wait for us to update you. As soon as there is any status change in your upload request, we will send a POST notification to the URL you sent.

You can add a callback URL as one of the parameters of the upload-video API (see below the description from the API). If you are not sure how to do it, you can check the code samples from our GitHub repo. By the way, for callback URL you can also use Azure Functions, a serverless event-driven platform that can be triggered by HTTP and implement a following flow.

5. Use the right indexing parameters for you

Probably the first thing you need to do when using Video Indexer, and specifically when trying to scale, is to think about how to get the most out of it with the right parameters for your needs. Think about your use case, by defining different parameters you can save yourself money and make the indexing process for your videos faster.

We are giving you the option to customize your usage in Video Indexer by choosing those indexing parameters. Don’t set the preset to streaming it if you don’t plan to watch it, don’t index video insights if you only need audio insights, it is that easy.

Before uploading and indexing your video read this short documentation, check the indexingPreset and streamingPreset parts to get a better idea of what your options are.

6. Index in optimal resolution, not highest resolution

Not too long ago, we were in times when HD videos didn't exist. Now, we have videos of varied qualities from HD to 8K. The question is, what video quality do you need for indexing your videos? The higher the quality of the movie you upload means the higher the file size, and this leads to higher computing power and time needed to upload the video.

Our experiences show that, in many cases, indexing performance has almost no difference between HD (720P) videos and 4K videos. Eventually, you’ll get almost the same insights with the same confidence.

For example, for the face detection feature, a higher resolution can help with the scenario where there are many small but contextually important faces. However, this will come with a quadratic increase in runtime (and therefore higher COGS) and an increased risk of false positives.

Therefore, we recommend you to verify that you get the right results for your use case and to first test it locally. Upload the same video in 720P and in 4K and compare the insights you get. Remember, No need to use a cannon to kill a fly.

Have questions or feedback? We would love to hear from you. Use our UserVoice page to help us prioritize features, leave a comment below or email VISupport@Microsoft.com for any questions.

We want to hear about your use case, and we can help you scale.
Quelle: Azure

Fueling intelligent energy with IoT

At Microsoft, building a future that we can all thrive in is at the center of everything we do. On January 16, as part of the announcement that Microsoft will be carbon negative by 2030, we discussed how advances in human prosperity, as measured by GDP growth, are inextricably tied to the use of energy. Microsoft has committed to deploy $1 billion into a new climate innovation fund to accelerate the development of carbon reduction and removal technologies that will help us and the world become carbon negative. The Azure IoT team continues to invest in the platforms and tools that enable solution builders to deliver new energy solutions, customers to empower their workforce, optimize digital operations and build smart, connected, cities, vehicles, and buildings.

Earlier, Microsoft committed $50 Million through Microsoft AI for Earth that provides technology, resources, and expertise into the hands of those working to solve our most complex global environmental challenges. Challenges like helping customers around the world meet their energy and sustainability commitments. Our partnership with Vattenfall illustrates how we will power new Swedish datacenter locations with renewable energy and our partnership with E.ON who manages low-voltage distribution grids is challenging the limits of traditional technology for low-voltage distribution grids through an inhouse IoT platform based on Microsoft Azure IoT Hub.

Over the past few years, our engineers have had the pleasure to connect with and learn from a large ecosystem of energy solution builders and customers that are proactively shifting their consumption priorities. Transmission system operators (TSOs) are focused on transforming grid operations while distribution system operators (DSOs) and utilities are approaching their customers with new solutions, and all participants are requesting better, more accurate, more secure data.

As millions of new electric vehicles are entering our roads, new challenges arise around the transformation of the energy grid that moves us in our daily commutes. At the heart of these transformations are solutions that help energy providers get connected, stay connected, and transform their businesses through devices, insights, and actions.

Late 2019, we announced updates to Azure IoT Central to help solution builders move beyond proof of concept to building business-critical applications they can brand and sell directly or through Microsoft AppSource. Builders can brand, customize, and make their own apps using extensibility via APIs, data connectors to business applications, repeatability, and manageability of their investment through multitenancy and seamless device connectivity. Two IoT Central energy app templates for solar panel and smart meter monitoring already help energy solution builders accelerate development.

Azure IoT Central Energy App Templates.

DistribuTECH 2020

DistribuTECH International is the leading annual transmission and distribution event that addresses technologies used to move electricity from the power plant through the transmission and distribution systems to the meter and inside the home. Held January 28 to January 30 in San Antonio, Texas, we invited 8 leading Energy solution builders to join us at DistribuTECH to demonstrate how they have leveraged Azure IoT to deliver amazing innovation. These partners will join Azure IoT Experts who are available to discuss your business scenarios or get more specific on IoT devices, working with IoT data and delivering a secure solution from the edge to the cloud.

Partners fueling intelligent energy

NXP EdgeVerse™ platform: intelligently manage grid load securely at the edge

The shift to vehicle electrification requires a completely different fueling infrastructure than gas-powered vehicles. Drivers of electric vehicles need to trust they can fuel for every occasion—everywhere, anytime and not get stranded. Every electric utility vehicle in a managed fleet, for example, must be authorized to charge without overloading the grid during peak times.

To manage grid load intelligently, edge computing and security becomes vital. NXP and Microsoft have demonstrated “Demand Side Management” of a smart electric vehicle charging grid and infrastructure running on NXP’s EdgeVerse™ using Azure IoT Central. This solution helps reduced development risk and speed time to market. NXP EdgeVerse includes the NXP Layerscape LS1012 processor and i.MX RT 1060 series, integrated in Scalys TrustBox Edge, to provide best-in-class power efficiency and the most secure (portable) level of communication solution that connects to Azure IOT Central. As the fueling model shifts from petroleum to electric, intelligent management of grid load balancing is key.

OMNIO.net: Danish IoT connectivity startup onboarding devices and unifying data

OMNIO.net, a Danish Industrial IoT connectivity startup, is partnering with Microsoft Azure IoT to solve two of the biggest hurdles in Industrial IoT: onboarding of devices and unification of data.

OMNIO.net is helping companies of all sizes who have outfitted their campuses with solar panels. The OMNIO.net solution connected these panels to Azure IoT Hub to gather real-time data that will help optimize energy production and limit downtime. Companies look to OMNIO.net to overcome challenges connecting industrial devices and getting the most from their data. What may have taken months in the past, with the combination of OMNIO.net’s energy expertise and Azure IoT offers device connection for partners in less than 24 hours, so customers can focus on using their data to solve pressing business challenges rather than on IT.

iGen Technologies: a self-powered heating system for your home

iGen Technologies’ i2 is a self-power heating system for residential homes. With its patented technology, i2 sets a new benchmark in home comfort and efficiency, by generating, storing and using its own electricity, keeping the heat on, even during a grid outage. The system delivers resilience, lower operating costs, efficiency gains, and greenhouse gas emission reductions. The fully integrated solution offers a dispatchable resource with fuel switching capability, providing utilities a valuable tool to manage peak load and surplus generation situations. iGEN has partnered with Microsoft Azure IoT Central to develop a smart IoT interface for the i2 heat and power system. The integration of iGEN’s distributed energy resource (DER) technology with Microsoft’s robust IoT app platform offers an ideal solution for utility Demand Response programs.

The i2 self-powered heating system. 

Agder Energi, NODES: scaling a sustainable and integrated energy marketplace

Distributed energy resources, digitalization, decarbonization, and new consumer behavior introduce challenges and opportunities for grid system operators to maintain reliable operation of the power system and create customer-centric services. The NODES marketplace relies on Azure to scale its flexible marketplace across 15 projects in 10 different European countries. The focus is on the use of flexibility from the distribution grid, transmission and distribution coordination, and integration with current balancing markets. Agder Energi is now piloting a flexible asset register and data hub with device management and analytics built on IoT Central. Rune Hogga, CEO of Agder Energi Flexibility, told us, "In order to have control of the data and be able to verify flexibility trades, Azure IoT Central provides us with a fast and efficient way to set up a system to collect data from a large number of distributed flexible assets."

L&T Technology Services: reducing carbon consumption and emissions

L&T Technology Services (LTTS) has developed low carbon and EV charging grid solutions for global enterprises, buildings, and smart cities. The LTTS Smart City, Campus & Building solutions enable reducing carbon emissions by up to 40 percent through its iBEMS on Azure solution by connecting an entire building's infrastructure to through single unified interface. In collaboration with Microsoft Real Estate & Facilities, LTTS is building breakthrough EV Charging Solutions providing actionable insights and usage patterns, demand forecasting, design and efficiency anomalies for Facility Managers on EV Charger assets while accurately tracking carbon credit. The LTTS solution also enables Facility Managers to optimize EV Charging Grid based on energy sources (geothermal, solar, electric) and grid constraints such as energy capacity, providing consumer EV charging notifications-based drive range preferences.

Telensa: utilities to support the business case for smart street lighting

Telensa makes wireless smart city applications, helping cities and utilities around the world save energy, work smarter, and deliver more cohesive services for their residents. Telensa is demonstrating how utilities can support the business case for smart street lighting, offering a platform to simply and seamlessly add other smart city applications like traffic monitoring, air quality and EV charging with AI-driven data insights. Telensa’s smart city solutions are increasingly built on Microsoft Azure IoT, leveraging the combination of data, devices, and connectivity, making IoT applications a practical proposition for any city.

Telensa is leading the Urban Data Project, with an initial deployment in Cambridge, UK. This new edge-AI technology is generating valuable insights from streetlight-based imaging, creating a trusted infrastructure for urban data to enable cities to collect, protect, and use their data for the benefit of all residents. The first deployment is in Cambridge, UK. Telensa’s Urban IQ, using Microsoft Power BI for data visualization is an open, low-cost platform to add multiple sensor applications.

 

Telensa’s streetlight based multi-sensor pods, which run on Azure IoT Edge and feature real-time AI and machine learning to extract insights.

eSmart Systems: improving powerline inspections and asset optimization by empowering human experts with Collaborative AI

eSmart Systems helps utilities gain insight into their assets by creating a virtuous cycle of collaboration and training between subject matter experts like Distribution or Transmission Engineers and state of the art deep learning artificial intelligence (AI).

A Microsoft finalist for AI energy partner of the year in 2019, eSmart’s Connected Drone software uses the Azure platform for accurate and self-improving power grid asset discovery and analysis. Grid inspectors continuously review results and correct them to feedback more accurate results to the system. Utilities can optimize visual data to improve their asset registries, reduce maintenance costs and improve reliability.

Kongsberg Digital: Grid Logic digital twin services for electrical grids

Increased electrification and introduction of intermittent, distributed, and renewable energy production challenge today’s grid operations. A lack of sufficient data and insights lead to over-investment, capacity challenges, and power quality issues. With Grid Logic digital twin services running on Azure, grid operators get forecasting and insights into hotspots and scenario simulation. With Azure IoT Hub, Grid Logic will make it possible to build a robust operating system for automation of real-time grid operation, optimization, and automation.

Grid Logic capacity heatmap for a part of Norwegian DSO BKK Nett’s grid.

Let’s connect and collaborate to build your energy solutions  

Microsoft Azure IoT is empowering businesses and industries to shape the future with IoT. We’re ready to meet and support you wherever you are in your transformation journey. Pairing a strong portfolio of products and partners will help you accelerate building robust IoT solutions, to achieve your goals. If you are attending DistribuTECH 2020, speak with Azure IoT experts, or connect with one of the partners mentioned above. 

Learn more about Microsoft Azure IoT and IoT for energy

Partner links:

Adger Energi
eSmart Igen
Konsgberg
L&T Technologies Services and L&T Power 
Nodes Market NXP
Omnio
Telensa

Quelle: Azure

MLOps—the path to building a competitive edge

Enterprises today are transforming their businesses using Machine Learning (ML) to develop a lasting competitive advantage. From healthcare to transportation, supply chain to risk management, machine learning is becoming pervasive across industries, disrupting markets and reshaping business models.

Organizations need the technology and tools required to build and deploy successful Machine Learning models and operate in an agile way. MLOps is the key to making machine learning projects successful at scale. What is MLOps ? It is the practice of collaboration between data science and IT teams designed to accelerate the entire machine lifecycle across model development, deployment, monitoring, and more. Microsoft Azure Machine Learning enables companies to fully embrace MLOps practices will and truly be able to realize the potential of AI in their business.

One great example of a customer transforming their business with Machine Learning and MLOps is TransLink. They support Metro Vancouver's transportation network, serving 400 million total boarding’s from residents and visitors as of 2018. With an extensive bus system spanning 1,800 sq. kilometers, TransLink customers depend heavily on accurate bus departure times to plan their journeys.

To enhance customer experience, TransLink deployed 18,000 different sets of Machine Learning models to better predict bus departure times that incorporate factors like traffic, bad weather, and other schedule disruptions. Using MLOps with Azure Machine Learning they were able to manage and deliver the models at scale.

“With MLOps in Azure Machine Learning, TransLink has moved all models to production and improved predictions by 74 percent, so customers can better plan their journey on TransLink's network. This has resulted in a 50 percent reduction on average in customer wait times at stops.”–Sze-Wan Ng, Director of Analytics & Development, TransLink.

Johnson Controls is another customer using Machine Learning Operations at scale. For over 130 years, they have produced fire, HVAC and security equipment for buildings. Johnson Controls is now in the middle of a smart city revolution, with Machine Learning being a central aspect of their equipment maintenance approach.

Johnson Controls runs thousands of chillers with 70 different types of sensors each, streaming terabytes of data. MLOps helped put models into production in a timely fashion, with a repeatable process, to deliver real-time insights on maintenance routines. As a result, chiller shutdowns could be predicted days in advance and mitigated effectively, delivering cost savings and increasing customer satisfaction.

“Using the MLOps capabilities in Azure Machine Learning, we were able to decrease both mean time to repair and unplanned downtime by over 66 percent, resulting in substantial business gains.”–Vijaya Sekhar Chennupati, Applied Data Scientist at Johnson Controls

Getting started with MLOps

To take full advantage of MLOps, organizations need to apply the same rigor and processes of other software development projects.

To help organizations with their machine learning journey, GigaOm developed the MLOps vision report that includes best practices for effective implementation and a maturity model.

Maturity is measured through five levels of development across key categories such as strategy, architecture, modeling, processes, and governance. Using the maturity model, enterprises can understand where they are and determine what steps to take to ‘level up’ and achieve business objectives.

 

 

“Organizations can address the challenges of developing AI solutions by applying MLOps and implementing best practices. The report and MLOps maturity model from GigaOm can be a very valuable tool in this journey,”– Vijaya Sekhar Chennupati, Applied Data Scientist at Johnson Controls.

To learn more, read the GigaOm report and make machine learning transformation a reality for your business.

More information

Learn more about Azure Machine Learning

Read the GigaOm report, Delivering on the Vision of MLOps

Try Azure Machine Learning for free today.

Quelle: Azure

Azure Data Explorer and Stream Analytics for anomaly detection

Anomaly detection plays a vital role in many industries across the globe, such as fraud detection for the financial industry, health monitoring in hospitals, fault detection and operating environment monitoring in the manufacturing, oil and gas, utility, transportation, aviation, and automotive industries.

Anomaly detection is about finding patterns in data that do not conform to expected behavior. It is important for decision-makers to be able to detect them and take proactive actions if needed. Using the oil and gas industry as one example, deep-water rigs with various equipment are intensively monitored by hundreds of sensors that send measurements in various frequencies and formats. Analysis or visualization is hard using traditional software platforms, and any non-productive time on deep-water oil rig platforms caused by the failure to detect anomaly could mean large financial losses each day.

Companies need new technologies like Azure IoT, Azure Stream Analytics, Azure Data Explorer and machine learning to ingest, processes, and transform data into strategic business intelligence to enhance exploration and production, improve manufacturing efficiency, and ensure safety and environmental protection. These managed services also help customers dramatically reduce software development time, accelerate time to market, provide cost-effectiveness, and achieve high availability and scalability.

While the Azure platform provides lots of options for anomaly detection and customers can choose the technology that best suits their needs, customers also brought questions to field facing architects on what use cases are most suitable for each solution. We’ll examine the answers to these questions below, but first, you’ll need to know a couple definitions:

What is a time series? A time series is a series of data points indexed in time order. In the oil and gas industry, most equipment or sensor readings are sequences taken at successive points in time or depth.

What is decomposition of additive time series? Decomposition is the task to separate a time series into components as shown on the graph below.

Time-series forecasting and anomaly detection

Anomaly detection is the process to identify observations that are different significantly from majority of the datasets.

This is an anomaly detection example with Azure Data Explorer.

The red line is the original time series.
The blue line is the baseline (seasonal + trend) component.
The purple points are anomalous points on top of the original time series.

To detect anomalies, either Azure Stream Analytics or Azure Data Explorer can be used for real-time analytics and detection as illustrated in the diagram below.

Azure Stream Analytics is an easy-to-use, real-time analytics service that is designed for mission-critical workloads. You can build an end-to-end serverless streaming pipeline with just a few clicks, go from zero to production in minutes using SQL, or extend it with custom code and built-in machine learning capabilities for more advanced scenarios.

Azure Data Explorer is a fast, fully managed data analytics service for near real-time analysis on large volumes of data streaming from applications, websites, IoT devices, and more. You can ask questions and iteratively explore data on the fly to improve products, enhance customer experiences, monitor devices, boost operations, and quickly identify patterns, anomalies, and trends in your data.

Azure Stream Analytics or Azure Data Explorer?

Use Case

Stream Analytics is for continuous or streaming real-time analytics, with aggregate functions support hopping, sliding, tumbling, or session windows. It will not suit your use case if you want to write UDFs or UDAs in languages other than JavaScript or C#, or if  your solution is in a multi-cloud or on-premises environment.

Data Explorer is for on-demand or interactive near real-time analytics, data exploration on large volumes of data streams, seasonality decomposition, ad hoc work, dashboards, and root cause analyses on data from near real-time to historical. It will not suit you use case if you need to deploy analytics onto the edge.

Forecasting

You can set up a Stream Analytics job that integrates with Azure Machine Learning Studio.

Data Explorer provides native function for forecasting time series based on the same decomposition model. Forecasting is useful for many scenarios like preventive maintenance, resource planning, and more.

Seasonality

Stream Analytics does not provide seasonality support, with the limitation of sliding windows size.

Data Explorer provides functionalities to automatically detect the periods in the time series or allows you to verify that a metric should have specific distinct period(s) if you know them.

Decomposition

Stream Analytics does not support decomposition.

Data Explorer provides function which takes a set of time series and automatically decomposes each time series to its seasonal, trend, residual, and baseline components.

Filtering and Analysis

Stream Analytics provides functions to detect spikes and dips or change points.

Data Explorer provides analysis to finds anomalous points on a set of time series, and a root cause analysis (RCA) function after anomaly is detected.

Filtering

Stream Analytics provides a filter with reference data, slow-moving, or static.

Data Explorer provides two generic functions:
•    Finite impulse response (FIR) which can be used for moving average, differentiation, shape matching
•    Infinite impulse response (IIR) for exponential smoothing and cumulative sum

Anomaly Detection

Stream Analytics provides detections for:
•    Spikes and dips (temporary anomalies)
•    Change points (persistent anomalies such as level or trend change)

Data Explorer provides detections for:
•    Spikes & dips, based on enhanced seasonal decomposition model (supporting automatic seasonality detection, robustness to anomalies in the training data)
•    Changepoint (level shift, trend change) by segmented linear regression
•    KQL Inline Python/R plugins enable extensibility with other models implemented in Python or R

What's next?

Azure Data Analytics, in general, brings you the best of breed technologies for each workload. The new Real-Time Analytics architecture (shown above) allows leveraging the best technology for each type of workload for stream and time-series analytics including anomaly detection. The following is a list of resources that may help you get started quickly:

If you haven't already, check out this GitHub repository for Anomaly detection in Azure Stream Analytics

Check out his GitHub repository for Anomaly detection and forecasting in Azure Data Explorer, and Time series analysis in Azure Data Explorer. 

Anomaly detection in Azure Stream Analytics Overview

Anomaly detection and forecasting in Azure Data Explorer Overview

Documentation on Time series analysis in Azure Data Explorer and this blog

Documentation on Kusto query language and Time Series Analysis 

Quelle: Azure

Microsoft Sustainability Calculator helps enterprises analyze the carbon emissions of their IT infrastructure

For more than a decade, Microsoft has been investing to reduce environmental impact while supporting the digital transformation of organizations around the world through cloud services. We strive to be transparent with our commitments, evidenced by our announcement that Microsoft’s cloud datacenters will be powered by 100 percent renewable energy sources by 2025. The commitments and investments we make as a company are important steps in reducing our own environmental impact, but we recognize that the opportunity for positive change is greatest by empowering customers and partners to achieve their own sustainability goals.

An industry first—the Microsoft Sustainability Calculator

Today we’re announcing the availability of the Microsoft Sustainability Calculator, a Power BI application for Azure enterprise customers that provides new insight into carbon emissions data associated with their Azure services. Migrating from traditional datacenters to cloud services significantly improves efficiencies, however, enterprises are now looking for additional insights into the carbon impact of their cloud workloads to help them make more sustainable computing decisions. For the first time, those responsible for reporting on and driving sustainability within their organizations will have the ability to quantify the carbon impact of each Azure subscription over a period of time and datacenter region, as well as see estimated carbon savings from running those workloads in Azure versus on-premises datacenters. This data is crucial for reporting existing emissions and is the first step in establishing a foundation to drive further decarbonization efforts.

Providing transparency with rigorous methodology

The tool’s calculations are based on a customer’s Azure consumption, informed by the research in the 2018 whitepaper, “The Carbon Benefits of Cloud Computing: a Study of the Microsoft Cloud”, and have been independently verified by Apex, a leading environmental verification body. The calculator factors in inputs such as the energy requirements of the Azure service, the energy mix of the electric grid serving the hosting datacenters, Microsoft’s procurement of renewable energy in those datacenters, as well as the emissions associated with the transfer of data over the internet. The result is an estimate of the greenhouse gas (GHG) emissions, measured in total metric tons of carbon equivalent (MTCO2e) related to a customer’s consumption of Azure.

The calculator gives a granular view of the estimated emissions savings from running workloads on Azure by accounting for Microsoft’s IT operational efficiency, IT equipment efficiency, and datacenter infrastructure efficiency compared to that of a typical on-premises deployment. It also estimates the emissions savings attributable to a customer from Microsoft’s purchase of renewable energy.
  

We also understand customers want transparency into the specific commitments we are making to build a more sustainable cloud. To make that information easily accessible, we’ve built a view within the tool of the renewable energy projects that Microsoft has invested in as part of its carbon neutral and renewable energy commitments. Each year Microsoft purchases renewable energy to cover its annual cloud consumption. Customers can use the world map to learn about projects in regions where they consume Azure services or have a regional presence. The projects are examples of the investments that Microsoft has made since 2012.

A path to actionable insight

Azure enterprise customers can get started by downloading the Microsoft Sustainability Calculator from AppSource now and following the included setup instructions. We’re excited by the opportunity this new tool provides for our customers to gain a deeper understanding of their current infrastructure and drive meaningful sustainability conversations within their organizations. We see this as a first step and plan to deepen and expand the tool’s capabilities in the future. We know our customers would like an even more comprehensive view of the sustainability benefits of our cloud services and look forward to supporting and enabling them in their journey.
Quelle: Azure

Creating a more accessible world with Azure AI

At Microsoft, we are inspired by how artificial intelligence is transforming organizations of all sizes, empowering them to reimagine what’s possible. AI has immense potential to unlock solutions to some of society’s most pressing challenges.

One challenge is that according to the World Health Association, globally, only 1 in 10 people with a disability have access to assistive technologies and products. We believe that AI solutions can have a profound impact on this community. To meet this need, we aim to democratize AI to make it easier for every developer to build accessibility into their apps and services, across language, speech, and vision.

In view of the upcoming Bett Show in London, we’re shining a light on how Immersive Reader enhances reading comprehension for people regardless of their age or ability, and we’re excited to share how Azure AI is broadly enabling developers to build accessible applications that empower everyone.

Empowering readers of all abilities

Immersive Reader is an Azure Cognitive Service that helps users of any age and reading ability with features like reading aloud, translating languages, and focusing attention through highlighting and other design elements. Millions of educators and students already use Immersive Reader to overcome reading and language barriers.

The Young Women’s Leadership School of Astoria, New York, brings together an incredible diversity of students with different backgrounds and learning styles. The teachers at The Young Women’s Leadership School support many types of learners, including students who struggle with text comprehension due to learning differences, or language learners who may not understand the primary language of the classroom. The school wanted to empower all students, regardless of their background or learning styles, to grow their confidence and love for reading and writing.

Watch the story here. 

Teachers at The Young Women’s Leadership School turned to Immersive Reader and an Azure AI partner, Buncee, as they looked for ways to create a more inclusive and engaging classroom. Buncee enables students and teachers to create and share interactive multimedia projects. With the integration of Immersive Reader, students who are dyslexic can benefit from features that help focus attention in their Buncee presentations, while those who are just learning the English language can have content translated to them in their native language.

Like Buncee, companies including Canvas, Wakelet, ThingLink, and Nearpod are also making content more accessible with Immersive Reader integration. To see the entire list of partners, visit our Immersive Reader Partners page. Discover how you can start embedding Immersive Reader into your apps today. To learn more about how Immersive Reader and other accessibility tools are fostering inclusive classrooms, visit our EDU blog.

Breaking communication barriers

Azure AI is also making conversations, lectures, and meetings more accessible to people who are deaf or hard of hearing. By enabling conversations to be transcribed and translated in real-time, individuals can follow and fully engage with presentations.

The Balavidyalaya School in Chennai, Tamil Nadu, India teaches speech and language skills to young children who are deaf or hard of hearing. The school recently held an international conference with hundreds of alumni, students, faculty, and parents. With live captioning and translation powered by Azure AI, attendees were able to follow conversations in their native languages, while the presentations were given in English.

Learn how you can easily integrate multi-language support into your own apps with Speech Translation, and see the technology in action with Translator, with support for more than 60 languages, today.

Engaging learners in new ways

We recently announced the Custom Neural Voice capability of Text to Speech, which enables customers to build a unique voice, starting from just a few minutes of training audio.

The Beijing Hongdandan Visually Impaired Service Center leads the way in applying this technology to empower users in incredible ways. Hongdandan produces educational audiobooks featuring the voice of Lina, China’s first blind broadcaster, using Custom Neural Voice. While creating audiobooks can be a time-consuming process, Custom Neural Voice allows Lina to produce high-quality audiobooks at scale, enabling Hongdandan to support over 105 schools for the blind in China like never before.

“We were amazed by how quickly Azure AI could reproduce Lina's voice in such a natural-sounding way with her speech data, enabling us to create educational audiobooks much more quickly. We were also highly impressed by Microsoft's commitment to protecting Lina's voice and identity."—Xin Zeng, Executive Director at Hongdandan

Learn how you can give your apps a new voice with Text to Speech.

Making the world visible for everyone

According to the International Agency for the Prevention of Blindness, more than 250 million people are blind or have low vision across the globe. Last month, in celebration of the United Nations International Day of Persons with Disabilities, Seeing AI, a free iOS app that describes nearby people, text, and objects, expanded support to five new languages. The additional language support for Spanish, Japanese, German, French, and Dutch makes it possible for millions of blind or low vision individuals to read documents, engage with people around them, hear descriptions of their surroundings in their native language, and much more. All of this is made possible with Azure AI.

Try Seeing AI today or extend vision capabilities to your own apps using Computer Vision and Custom Vision.

Get involved

We are humbled and inspired by what individuals and organizations are accomplishing today with Azure AI technologies. We can’t wait to see how you will continue to build on these technologies to unlock new possibilities and design more accessible experiences. Get started today with a free trial.

Check out our AI for Accessibility program to learn more about how companies are harnessing the power of AI to amplify capabilities for the millions of people around the world with a disability.
Quelle: Azure