Kategorie: Microsoft Azure

Keeping your cloud deployments secure during challenging times

As the world comes together to combat COVID-19, and remote work becomes a critical capability for many companies, customers have asked us how to best maintain the security posture of their cloud assets while enabling more remote workers to access them.

Misconfiguration of cloud security controls has been at the root of several recent data breaches, so it’s extremely important to continue monitoring your security posture as usage of cloud assets increases.

To help you prioritize the actions that you need to take, we are listing three common scenarios for remote workers and how to leverage Azure Security Center security controls to prioritize relevant recommendations for these scenarios:

1. As more users need to access resources remotely, you need to ensure that Multi-Factor Authentication (MFA) is enabled to enhance their identity protection.

Azure Security Center has a security control called Enable MFA, ideally you should remediate all recommendations that are part of this security control, as shown below:

2. Some users might need remote access via RDP or SSH to servers that are in your Azure infrastructure.

Instead of allowing full 24 x 7 access to those servers, ensure that you are using Just-In-Time (JIT) VM access to those servers. Make sure to review the Secure management ports control in Azure Security Center and remediate the recommendations that are relevant for this scenario.

3. Some of the workloads (servers, containers, databases) that will be accessed remotely by users might be missing critical security updates.

Review the Remediate vulnerabilities control in Azure Security Center to prioritize the updates that must be installed. Make sure to review the result of all recommendations in built-in vulnerability assessment and remediate those items.

Security posture management is an ongoing process. Review your secure score to understand your progress towards a fully compliant environment.

Users of Azure are likely just a portion of your user base. Below is additional guidance on enabling and securing remote work for the rest of your organization:

The top 9 ways Microsoft IT is enabling remote work for its employees
Staying productive while working remotely with Microsoft Teams
Working remotely during challenging times
Work remotely, stay secure—guidance for CISOs

Quelle: Azure

Microsoft powers transformation at NVIDIA’s GTC Digital Conference

The world of supercomputing is evolving. Work once limited to high-performance computing (HPC) on-premises clusters and traditional HPC scenarios, is now being performed at the edge, on-premises, in the cloud, and everywhere in between. Whether it’s a manufacturer running advanced simulations, an energy company optimizing drilling through real-time well monitoring, an architecture firm providing professional virtual graphics workstations to employees who need to work remotely, or a financial services company using AI to navigate market risk, Microsoft’s collaboration with NVIDIA makes access to NVIDIA graphics processing units (GPU) platforms easier than ever.

These modern needs require advanced solutions that were traditionally limited to a few organizations because they were hard to scale and took a long time to deliver. Today, Microsoft Azure delivers HPC capabilities, a comprehensive AI platform, and the Azure Stack family of hybrid and edge offerings that directly address these challenges.

This year during GTC Digital, we’re spotlighting some of the most transformational applications powered by NVIDIA GPU acceleration that highlight our commitment to edge, on-prem, and cloud computing. Registration is free, so sign up to learn how Microsoft is powering transformation.

Visualization and GPU workstations

Azure enables a wide range of visualization workloads, which are critical for desktop virtualization as well as professional graphics such as computer-aided design, content creation, and interactive rendering. Visualization workloads on Azure are powered by NVIDIA’s world-class GPUs and Quadro technology, the world’s preeminent visual computing platform. With access to graphics workstations on Azure cloud, artists, designers, and technical professionals can work remotely, from anywhere, and from any connected device. See our NV-Series virtual machines (VMs) for Windows and Linux.

Artificial intelligence

We’re sharing the release of the updated execution provider in ONNX Runtime with integration for NVIDIA TensorRT 7. With this update, ONNX Runtime can execute open Open Neural Network Exchange (ONNX) models on NVIDIA GPUs on Azure cloud and at the edge using the Azure Stack Edge, taking advantage of the new features in TensorRT 7 like dynamic shape, mixed precision optimizations, and INT8 execution.

Dynamic shape support enables users to run variable batch size, which is used by ONNX Runtime to process recurrent neural network (RNN) and bit error test rate (BERT) models. Mixed precision and INT8 execution are used to speed up execution on the GPU, which enables ONNX Runtime to better balance the performance across CPU and GPU. Originally released in March 2019, TensorRT with ONNX Runtime delivers better inferencing performance on the same hardware when compared to generic GPU acceleration.

Additionally, the Azure Machine Learning service now supports RAPIDS, a high-performance GPU execution accelerator for data science framework using the NVIDIA CUDA platform. Azure developers can use RAPIDS in the same way they currently use other machine learning frameworks, and in conjunction with Pandas, Scikit-learn, PyTorch, and TensorFlow. These two developments represent major milestones towards a truly open and interoperable ecosystem for AI. We’re working to ensure these platform additions will simplify and enrich those developer experiences.

Edge

Microsoft provides various solutions in the Intelligent Edge portfolio to empower customers to make sure that machine learning not only happens in the cloud but also at the edge. The solutions include Azure Stack Hub, Azure Stack Edge, and IoT Edge.

Whether you are capturing sensor data and inferencing at the Edge or performing end-to-end processing with model training in Azure and leveraging the trained models at the edge for enhanced inferencing operations Microsoft can support your needs however and wherever you need to.

Supercomputing scale

Time-to-decision is incredibly important with a global economy that is constantly on the move. With the accelerated pace of change, companies are looking for new ways to gather vast amounts of data, train models, and perform real-time inferencing in the cloud and at the edge. The Azure HPC portfolio consists of purpose-built computing, networking, storage, and application services to help you seamlessly connect your data and processing needs with infrastructure options optimized for various workload characteristics.

Azure Stack Hub announced preview

Microsoft, in collaboration with NVIDIA, is announcing that Azure Stack Hub with Azure NC-Series Virtual Machine (VM) support is now in preview. Azure NC-Series VMs are GPU-enabled Azure Virtual Machines available on the edge. GPU support in Azure Stack Hub unlocks a variety of new solution opportunities. With our Azure Stack Hub hardware partners, customers can choose the appropriate GPU for their workloads to enable Artificial Intelligence, training, inference, and visualization scenarios.

Azure Stack Hub brings together the full capabilities of the cloud to effectively deploy and manage workloads that otherwise are not possible to bring into a single solution. We are offering two NVIDIA enabled GPU models during the preview period. They are available in both NVIDIA V100 Tensor Core and NVIDIA T4 Tensor Core GPUs. These physical GPUs align with the following Azure N-Series VM types as follows:

NCv3 (NVIDIA V100 Tensor Core GPU): These enable learning, inference and visualization scenarios. See Standard_NC6s_v3 for a similar configuration.
TBD (NVIDIA T4 Tensor Core GPU): This new VM size (available only on Azure Stack Hub) enables light learning, inference, and visualization scenarios.

Hewlett Packard Enterprise is supporting the Microsoft GPU preview program as part of its HPE ProLiant for Microsoft Azure Stack Hub solution.“The HPE ProLiant for Microsoft Azure Stack Hub solution with the HPE ProLiant DL380 server nodes are GPU-enabled to support the maximum CPU, RAM, and all-flash storage configurations for GPU workloads,” said Mark Evans, WW product manager, HPE ProLiant for Microsoft Azure Stack Hub, at HPE. “We look forward to this collaboration that will help customers explore new workload options enabled by GPU capabilities.” 

As the leading cloud infrastructure provider1, Dell Technologies helps organizations remove cloud complexity and extend a consistent operating model across clouds. Working closely with Microsoft, the Dell EMC Integrated System for Azure Stack Hub will support additional GPU configurations, which include NVIDIA V100 Tensor Core GPUs, in a 2U form factor. This will provide customers increased performance density and workload flexibility for the growing predictive analytics and AI/ML markets. These new configurations also come with automated lifecycle management capabilities and exceptional support.

To participate in the Azure Stack Hub GPU preview, please send us an email today. 

Azure Stack Edge preview

We also announced the expansion of our Microsoft Azure Stack Edge preview with the NVIDIA T4 Tensor Core GPU. Azure Stack Edge is a cloud managed appliance that provides processing for fast local analysis and insights to the data. With the addition of an NVIDIA GPU, you’re able to build in the cloud then run at the edge. For more information about this exciting release please see the detailed blog.

GTC Digital

Microsoft session recordings will be available on the GTC Digital site starting March 26. You can find a list of the Microsoft digital sessions along with corresponding links in the Microsoft Tech Community blog here.

1 IDC WW Quarterly Cloud IT Infrastructure Tracker, Q3 2019, January 2020, Vendor Revenue
Quelle: Azure

Microsoft is expanding the Azure Stack Edge with NVIDIA GPU preview

We’re expanding the Microsoft Azure Stack Edge with NVIDIA T4 Tensor Core GPU preview during the GPU Technology Conference (GTC Digital). Azure Stack Edge is a cloud-managed appliance that brings Azure’s compute, storage, and machine learning capabilities to the edge for fast local analysis and insights. With the included NVIDIA GPU, you can bring hardware acceleration to a diverse set of machine learning (ML) workloads.

What’s new with Azure Stack Edge

At Mobile World Congress in November 2019, we announced a preview of the NVIDIA GPU version of Azure Stack Edge and we’ve seen incredible interest in the months that followed. Customers in industries including retail, manufacturing, and public safety are using Azure Stack Edge to bring Azure capabilities into the physical world and unlock scenarios such as the real-time processing of video powered by Azure Machine Learning.

These past few months, we’ve taken our customers' feedback to make key improvements and are excited to make our preview available to even more customers today.

If you’re not already familiar with Azure Stack Edge, here are a few of the benefits:

Azure Machine Learning: Build and train your model in the cloud, then deploy it to the edge for FPGA or GPU-accelerated inferencing.
Edge Compute: Run IoT, AI, and business applications in containers at your location. Use these to interact with your local systems, or to pre-process your data before it transfers to Azure.
Cloud Storage Gateway: Automatically transfer data between the local appliance and your Azure Storage account.  Azure Stack Edge caches the hottest data locally and speaks file and object protocols to your on-prem applications.
Azure-managed appliance: Easily order and manage Azure Stack Edge from the Azure Portal.  No initial capex fees; pay as you go, just like any other Azure service.

Enabling our partners to bring you world-class business applications

Equally important to bringing you a great device is enabling our partners to bring you innovative applications to meet your business needs.  We’d love to share some of the continued investment we’re making with partners to bring their exciting developments to you.

As self-checkouts grow in prevalence, Malong Technologies is innovating in AI applications for loss prevention.

“For our customers in the retail industry, artificial intelligence innovation is happening at the edge,” said Matt Scott, co-founder and chief executive officer, Malong Technologies. “Along with our state-of-the-art solutions, our customers need hardware that is powerful, reliable, and custom-tailored for the cloud. Microsoft’s Azure Stack Edge fits the bill perfectly. We’re proud to be a Microsoft Gold Certified Partner, working with Microsoft to help our retail customers succeed.”

Increasing your manufacturing organization’s quality inspection accuracy is key to Mariner’s Spyglass Visual Inspection application.

“Mariner has standardized on Microsoft’s Azure Stack Edge for our Spyglass Visual Inspection and Spyglass Connected Factory products. These solutions are mission critical to our manufacturing customers. Azure Stack Edge provides the performance, stability and availability they require.” – Phil Morris, CEO, Mariner

Building computer vision solutions to improve performance and safety in manufacturing and other industries is a key area of innovation for XXII.

“XXII is thrilled to be a Microsoft partner and we are working together to provide our clients with real time video analysis software on edge with the Azure Stack Edge box. With this solution, Azure allow us to harvest the full potential of NVIDIA GPUs directly on edge and be able to provide our clients in retail, industry and smart city with smart video analysis that are easily deployable, scalable and easily manageable with Azure stack Edge.” – Souheil Hanoune, Chief Scientific Officer, XXII

More to come with Azure Stack Edge

There are even more exciting developments with Azure Stack Edge coming. We’re putting the final touches on much-awaited new compute and AI capabilities including virtual machines, Kubernetes clusters, and multi-node support. Along with these new features announced at Ignite 2019, Data Box Edge was renamed Azure Stack Edge to align with the Azure Stack portfolio.

Our Rugged series for sites with harsh or remote environments is also coming this year, including the battery-powered form-factor that can be carried in a backpack. The versatility of these Azure Stack Edge form-factors and cloud-managed capabilities brings cloud intelligence and compute to retail stores, factory floors, hospitals, field operations, disaster zones, and rescue operations.

Get started with the Azure Stack Edge with NVIDIA GPU preview

Thank you for continuing to partner with us as we bring new capabilities to Azure Stack Edge. We’re looking forward to hearing from you.

To get started with the preview, please email us and we’ll follow up to learn more about your scenarios.
Learn more about Azure Stack Edge.

Learn more about Azure’s Hybrid Strategy

Read about more updates from Azure during NVIDIA’s GTC.
Quelle: Azure

How Azure Machine Learning service powers suggested replies in Outlook

Microsoft 365 applications are so commonplace that it’s easy to overlook some of the amazing capabilities that are enabled with breakthrough technologies, including artificial intelligence (AI). Microsoft Outlook is an email client that helps you work efficiently with email, calendar, contacts, tasks, and more in a single place.

To help users be more productive and deliberate in their actions while emailing, the web version of Outlook and the Outlook for iOS and Android app have introduced suggested replies, a new feature powered by Azure Machine Learning service. Now when you receive an email message that can be answered with a quick response, Outlook on the web and the Outlook mobile suggest three response options that you can use to reply with only a couple of clicks or taps, helping people communicate in both their workplace and personal life, by reducing the time and effort involved in replying to an email.

The developer team behind suggested replies is comprised of data scientists, designers, and machine learning engineers with diverse backgrounds who are working to improve the lives of Microsoft Outlook users by expediting and simplifying communications. They are at the forefront of applying cutting-edge natural language processing (NLP) and machine learning (ML) technologies and leverage these technologies to understand how users communicate through email and improve those interactions from a productivity standpoint to create a better experience for users.

A peek under the hood

To process the massive amount of raw data that these interactions provide, the team uses Azure Machine Learning pipelines to build their training models. Azure Machine Learning pipelines allow the team to divide training steps into discrete steps such as data cleanup, transforms, feature extraction, training, and evaluation. The output of the Azure Machine Learning pipeline converts raw data into a model. This Machine Learning pipeline allows the data scientists to build a training pipeline in a compliant manner that enforces privacy and compliance checks.

In order to train this model, the team needed a way to build and prepare a large data set comprised of over 100 million messages. To do this, the team leveraged a distributed processing framework to sample and retrieve data from a broad user base.

Azure Data Lake Storage is used to store the training data used for training the suggested replies models. We then clean and curate the data into message reply pairs (including potential responses to an email) that are stored in Azure Data Lake Storage (ADLS). The training pipelines also consume the reply pairs stored in ADLS in order to train models. To conduct the Machine Learning training itself, the team uses GPU pools available in Azure. The training pipelines leverage these curated Message Reply pairs to learn how to suggest appropriate replies based on a given message. Once the model is created, data scientists can compare the model performance with previous models and evaluate which approaches perform better at recommending relevant suggested replies.

The Outlook team helps protect your data by using the Azure platform to prepare large-scale data sets that are required to build a feature like suggested replies in accordance with Office 365 compliance standards. The data scientists use Azure compute and workflow solutions that enforce privacy policies to create experiments and train multiple models on GPUs. This helps with the overall developer experience and provides agility in the inner development loop cycle.

This is just one of many examples of how Microsoft products are powered by the breakthrough capabilities of Azure AI to create better user experiences. The team is learning from feedback every day and improving the feature for users while also expanding the types of suggested replies offered. Keep following the Azure blog to stay up-to-date with the team and be among the first to know when this feature is released.

Learn more

Learn more about the Azure Machine Learning service.

Get started with a free trial of Azure Machine Learning service.
Quelle: Azure

New Deploy to Azure extension for Visual Studio Code

Organizations and teams that adopt DevOps methodologies are consistently seeing improvements in their ability to deliver high-quality code, with faster release cycles, and ultimately achieve higher level of satisfaction for their own customers, whether they’re internal or external. Continuous Integration and Continuous Delivery (CI/CD) is one of the pillars of DevOps, consisting in automatically building, testing and deploying applications, but setting up a full CI/CD pipeline can be a complex task.

Today, we’re sharing the launch of the Deploy to Azure extension for Visual Studio Code. This new extension allows developers working in Visual Studio Code to seamlessly create, build, and deploy their apps in a continuous manner to the cloud, without leaving the editor.

Deploy to Azure extension

The Deploy to Azure extension works with both GitHub Actions and Azure Pipelines. It helps developers by auto-generating a CI/CD pipeline definition that takes care of building and deploying your app to the cloud with Azure. You can use Deploy to Azure extension to deploy application code present in your local system, or in Azure Repos or GitHub. We plan to expand the scope to other Git repositories in future.

You can use this extension to set up CI/CD pipeline for every code push. It will give you an auto-generated and fully-customizable CI/CD pipeline, defined in a YAML file that is formatted for either GitHub Actions or Azure Pipelines. The YAML file is pre-populated with build and release tasks, which can be edited by the developers as needed.

In the workflow, we are also setting up Azure Pipelines and GitHub with relevant Azure-related configurations, as well as repository-related configurations, without you needing to do worry about the plumbing of the systems.

Installation and usage

The Deploy to Azure extension can be downloaded for free from the Visual Studio Code Marketplace. After installing it, you can invoke it from the Command Palette (Ctrl + P or Cmd + P) > Deploy to Azure: Configure Pipeline.

Once you run the pipeline creation workflow, the extension will inspect your application’s code and generate a pipeline optimized for your project.

In this first release, the Deploy to Azure extension in Visual Studio Code supports generating pipelines to deploy Node.js-based apps to Azure App Service or Azure Functions App, as well as any containerized application (with a Dockerfile) to Azure Kubernetes Service.

We’re working on adding support for creating workflows for other languages, starting with Python, and for other Azure resources. Additionally, we will roll out support for other Git repository providers; in addition to GitHub and Azure Repos which are available today, we’re working on supporting source code on BitBucket and other locations.

We will also roll out support for other Git repository providers; in addition to GitHub and Azure Repos which are available today, we’re working on supporting source code on BitBucket and other locations.

Get started

You can get started today by installing the extension. Then, start adding CI/CD pipelines to your apps and have them deployed to the cloud continuously.

Please let us know your thoughts on this extension and how it helps your workflows, and anything we can do to improve your experience. You can connect with us on the extension’s project page on GitHub.
Quelle: Azure

Unified network monitoring with Connection Monitor now in preview

Azure Network Watcher’s new and improved Connection Monitor now provides unified end-to-end connection monitoring capabilities for hybrid and Azure deployments. Users can now use the same solution to monitor connectivity for on-premises, Azure, and multi-cloud setups. In this preview phase, the solution brings together the best of two key capabilities—Network Watcher's Connection Monitor and Network Performance Monitor's (NPM) Service Connectivity Monitor. Check out the documentation and start using Connection Monitor to check connectivity in your network.

The monitoring question

Customers have long stressed over the need for unified connection monitoring for hybrid deployments, where complex applications transact across Azure, on-premises, and with other public applications to deliver business-critical functionality. These challenges escalate in multi-cloud environments. Monitoring teams then wrestle with basic challenges including:

Which monitoring solution to use in these complex set-ups?
Do I need different monitoring solutions for on-premises and Azure or any other clouds?
Where does my data go and how do I correlate data from multiple sources?
How do I get the fastest alerts when things go wrong in my network?

Connection Monitor in preview

With the new Connection Monitor, you can now configure both Azure and non-Azure virtual machines and hosts for monitoring connectivity to global endpoints from a single console. You can set up Connection Monitor and create multiple test groups for various use cases including connectivity between Azure regions, connectivity to Office 365, and connectivity between app and database tiers. With the ability to add multiple sources and destinations in one test group, configuring monitoring gets much easier. You also benefit from an aggregated view of your network parameters, with the ability to drill down to individual links at the time of troubleshooting.

You can monitor loss and latency of network connections both within Azure and between Azure and external destinations, and view the topology to localize issues. The solution identifies the top five tests in your Connection Monitor, test groups, sources, and destinations, then highlights potential problem tests. For Azure resources, issues with your hops are shown in the topology.

Alerts and data storage

Monitoring data is stored in both Azure Monitor as metrics and in Log Analytics workspaces. You can now set up fast, metrics-based alerts to react to issues expeditiously. To build additional correlations on your historical data, use Log Analytics queries.

Other benefits

Single console for configuring and monitoring connectivity and network quality from Azure and on-premises virtual machines and hosts.
Monitor multiple endpoints within and across Azure regions, on-premises sites, and global service locations.
Higher and configurable probing frequencies.
More protocols supported to give better visibility into network performance.
Cross-region, cross-workspace monitoring.
Access to historical monitoring data retained in Log Analytics.
Rich user experience.
Automation through PowerShell and CLI.

Start monitoring today

The new Connection Monitor feature will be available for no charge during preview. The general availability pricing for Connection Monitor will be available soon on the pricing page. For more details, please visit the Connection Monitor (Preview) documentation.

We're here for you

We would love to hear from you. Send us your suggestions via the User Voice page.
Quelle: Azure

New features for Form Recognizer now available

Extracting text and structure information from documents is a core enabling technology for robotic process automation and workflow automation. Since its preview release in May 2019, Azure Form Recognizer has attracted thousands of customers to extract text, key and value pairs, and tables from documents to accelerate their business processes.

Today, we're sharing the new Form Recognizner features that are available.

Updates for Azure Form Recognizer

The Form Recognizer March release is a major update that includes many new features our customers have asked for:

Customization: The service now supports training with and without labels, which makes it easier for customers to reliably extract valuable information from their forms. The APIs have also been redesigned as long-running operations to improve support for larger customer data sets. Automatic detection of key value pairs and table extraction have been enhanced and improved. A new sample labeling tool UX container will help customers label data more efficiently and extract the values of interest.
  

Form Recognizer Custom: Train with Labels, Form Recognizer Sample Labeling Tool.

In addition, Form Recognizer Sample Labeling Tool is now available as an open source project located here. You can integrate it within your solutions and make customer-specific changes to meet your needs.

Layout: We released a new Layout API that is capable of extracting text and tables from documents with high accuracy optical character recognition (OCR) results on small texts. It also extracts tables from arbitrary documents, enabling a very popular application scenario for document extraction.

Layout text and table extraction: Table extracted with 5 columns and 30 rows.

Pre-Built Receipt: The new version features major accuracy improvements. Error rates for certain fields like merchant name, phone number, transaction time, and subtotal have been reduced by more than 30 percent. We also added support for recognizing tips, receipt type, and line items, as well as providing confidence values.
   

Pre-built Receipt: Key fields extracted from itemized sales receipt.

Learn more on what’s new in Form Recognizer here.

Our customers

Acumatica and Zelros are customers using Azure Form Recognizer and have shared their experiences with Microsoft.

“By automating expense reporting with Form Recognizer, we can eliminate almost all human errors—which really helps accounting teams streamline approvals and reimbursement.“ Ajoy Krishnamoorthy, Vice President of Platform and Technology Acumatica.

Learn more in our case study with Acumatica here.

“Zelros Documents2Insights leverages Form Recognizer to speed up the insurers' and bancassurers’ underwriting process. Identity card, proof of residence, vehicle registration document, driving license, and more. Speeding up and simplifying this business process is key to improve the experience of policyholders. Zelros Documents2Insights automates the underwriting processes, based on the Cognitive Services Computer Vision API and built on top of the Form Recognizer feature, the solution automatically reads and analyzes documents. It also cross-references information in order to correct and lower the error rate, while complying with regulatory requirements. With this, we are to process documents and subscriptions faster.”  Fabien Vauchelles, CTO of Zelros

Getting started

To get started, please login to the Azure Portal to create a Form Recognizer resource. Once your resource is created you can extract data from your forms by following one of our Quickstart templetes:

Custom: Train a custom model for your forms to extract text, key value pairs, and tables.

Train without labels:

Quickstart: Train a Form Recognizer model and extract form data by using the REST API with cURL.
Quickstart: Train a Form Recognizer model and extract form data by using the REST API with Python.

Train with labels:

Quickstart: Train a Form Recognizer model with labels using the sample labeling tool.
Quickstart: Train a Form Recognizer model with labels using REST API and Python.

Prebuilt receipts: Extract data from USA sales receipts.

Quickstart: Extract receipt data using the REST API with cURL.
Quickstart: Extract receipt data using the REST API with Python.

Layout: Extract text and table structure (row and column numbers) from your documents.

 Quickstart: Extract layout data using the REST API with Python.

Quelle: Azure

Learn new strategies and technologies to optimize your hybrid cloud

IT environments are becoming more complex as organizations are combining on-premises, cloud, and edge infrastructures. There are some major benefits to having a flexible, hybrid IT environment, such as the ability to create new business value while also meeting local and industry compliance requirements, but the headaches of managing and securing these environments are hard to ignore. But, with a solid strategy and the right tools, there’s enormous potential for innovation and growth with a hybrid environment.

This is why we're sharing the upcoming one-hour Azure Hybrid Virtual Event on Tuesday, March 31, 2020 starting at 8:00 AM Pacific Time. At this free online event, you’ll get to watch demos, learn hybrid best practices, and find out which strategies work—and which don’t—from two real Azure hybrid customers: online retailer ASOS and professional services company KPMG. Julia White, Corporate Vice President of Microsoft Azure Marketing, will kick off the event with a keynote on current and future hybrid cloud trends, followed by some great sessions:

Insights from Bain & Company—building a successful hybrid cloud: Hear from Bill Radzevych, a partner at Bain & Company, about market trends and customer insights with digital transformation and cloud adoption. 
Seamlessly manage and govern resources: Learn how to seamlessly manage, govern, and secure resources across on-premises, multicloud, and the edge from a single control plane.
Bring cloud services to any infrastructure: Learn how to bring cloud services to your existing infrastructure to take advantage of cloud innovation everywhere and discuss real-world examples from companies like KPMG.
Modernize your datacenter: Learn how to modernize virtualized apps or bring cloud to your datacenter while meeting regulatory and data sovereignty requirements.
Bring AI to the edge: Learn about different ways to take advantage of edge computing to create new business opportunities.
Secure your organization: Hear from George Mudie, Chief Information Security Officer from ASOS, on how Azure Sentinel empowers their SecOps to improve organizational security and efficiency.

 

See you there

Azure Hybrid Virtual Event: Tuesday, March 31, 2020 from 8:00 AM to 9:00 AM Pacific Time.

Delivered in partnership with Intel.

Quelle: Azure