Get the best value in your cloud journey with Azure pricing offers and resources

Cloud computing continues to transform the way businesses operate, innovate, and compete. And whether you’re just moving to the cloud or already have an established cloud footprint, you may have questions about how to pay for the services you need, estimate your costs, or optimize your spending. To help answer these questions, Azure provides a variety of resources and offers to help you get the best value at every stage of your cloud journey. 

Read More: DP-500: Designing and Implementing Enterprise-Scale Analytics Solutions Using Microsoft Azure and Microsoft Power BI

This blog post will show you how to approach and think about pricing throughout your cloud adoption journey. We will also give an example of how a hypothetical digital media company would approach their Azure pricing needs as they transition from evaluating and planning to setting up and running their cloud solutions. After reading this post, you will know more about how to select the best Azure pricing option for your business objectives and cloud needs.

Find guidance and resources to navigate Azure pricing options

If you are new to Azure or cloud computing in general, you may want to learn the basics of how cloud services are priced, and what options you have for paying for them. Azure offers a variety of pricing options to suit different needs and scenarios, from free tier and pay-as-you-go to commitment and benefits. Here’s a brief overview of each option: 

Free tier: You can get started with Azure for free, and access over 25 services for 12 months, plus $200 credit to use in your first 30 days. You can also use some services for free, such as Azure App Service, Azure Functions, and Azure DevOps, with certain limits and conditions. The free tier is a great way to explore Azure and learn how it works, without any upfront costs or commitments. 

Pay-as-you-go: You can pay only for the services you use or consume, based on the measured usage and the unit prices of each service. For example, you can pay for the number of virtual machine (VMs) hours, the amount of storage space, or the volume of data transferred. Pay-as-you-go is a flexible and scalable option that lets you adjust your usage and costs according to your changing needs and demands. 

Estimate Azure project costs 

If you have a new project to migrate to or build in Azure, you need an accurate and realistic estimate of your project costs to make an informed decision about moving forward. To help with this decision, Azure provides several tools and resources, such as: 

TCO calculator: You can use the Total Cost of Ownership (TCO) calculator to estimate how much you can save by migrating your on-premises workloads to Azure. You can input your current infrastructure details, such as servers, storage, and network, and see a detailed comparison of the costs of running them on-premises versus on Azure. 

Azure Migrate: You can use Azure Migrate to assess and plan your migration to Azure. You can discover and evaluate your on-premises servers, databases, and applications, and get recommendations on the best Azure services and sizing options for them. You can also get estimated costs and savings for your migration scenario and track your progress and readiness. 

Azure Architecture Center: You can get guidance for architecting solutions on Azure using established patterns and practices such as OpenAI Chatbots, Windows VM Deployment, and Analytics end-to-end with Azure Synapse with cost factors included. 

Calculate costs of Azure products and services 

If you are ready to deploy specific Azure services and you want to budget for them, you may want to consider the different pricing options and offers that are available for each service. Azure provides resources and guidance on how to budget for specific Azure services, such as: 

Azure pricing calculator: Estimate your monthly costs based on your expected usage and configuration such as region or virtual machine series. 

Product pricing details pages: Find detailed pricing information for each Azure service on its pricing details page. You can see the pricing model, the unit prices, the service tiers, and the regional availability.

Azure savings plan for compute: An easy and flexible way to save up to 65% on select compute services, compared to pay-as-you-go prices. The savings plan unlocks lower prices on compute services when you commit to spend a fixed hourly amount for one or three years. You choose whether to pay all upfront or monthly at no extra cost. 

Azure reservations: Reserve Azure resources, such as VMs, SQL Database, or Cosmos DB, for one or three years and save up to 72% on your cloud costs. Improve budgeting and forecasting with a single upfront payment that makes it easy to calculate your investments. Or lower your upfront cash outflow with a monthly payment option at no additional cost. 

Azure Hybrid Benefit: Apply your existing Windows Server, SQL Server licenses with active Software Assurance or subscriptions to Azure Hybrid Benefit to achieve cost savings. Save up to 85% compared to standard pay-as-you-go rates and achieve the lowest cost of ownership when you combine Azure Hybrid Benefit, reservations savings, and Extended Security Updates. You can also apply your active Linux subscription to Azure Hybrid Benefit. 

Manage and optimize your Azure investments 

If you are already using Azure and you want to optimize your spend for your current Azure workloads, you may want to review your usage and costs, and look for ways to enhance your investments. Azure provides several tools and resources to help you with this process, such as: 

Microsoft Cost Management: You can use Microsoft Cost Management with Copilot to monitor and analyze your Azure spending, and to create and manage budgets and alerts. You can see your current and forecasted costs, your cost trends and anomalies, and your cost breakdown by service, resource group, or subscription. You can also get recommendations on how to optimize your costs. 

Azure Advisor: You can use Azure Advisor to get personalized and actionable recommendations on how to improve the performance, security, reliability, and cost-effectiveness of your Azure resources. You can see the potential savings and benefits of each recommendation and apply them with a few clicks. 

FinOps on Azure: You can leverage FinOps best practices on Azure to empower your organization by fostering a culture of data-driven decision-making, accountability, and cross-team collaboration. This approach will help you maximize investments and accelerate business growth through improved organizational alignment 

An example of a company’s cloud journey and pricing needs 

To illustrate how a customer can choose the best pricing option and resources for their cloud journey, let’s look at an example. Contoso, a hypothetical digital media company, wants to migrate their infrastructure and build a new OpenAI Chatbot application in Azure. Here’s how they would think about their Azure pricing needs at each stage of their journey: 

Considering Azure: Contoso wants to understand how Azure pricing works. They use the free tier to try out some Azure services to test functionality. They also leverage the pay-as-you-go model to explore how some services are billed. 

Assess and plan Azure projects: Contoso needs to estimate their project costs. To compare the costs of running on-premises versus on Azure they input their on-premises server infrastructure in the TCO calculator. They also use the Azure Architecture Center to learn how to develop an OpenAI chatbot with best practices.

Deployment in Azure: Contoso is ready to migrate their environment and deploy their company’s chatbot app and wants to budget for the specific Azure services needed. They leverage the product specific pricing pages and the pricing calculator to estimate their monthly costs based on their expected usage and configuration. They purchase Reservations for their stable and predictable VMs and Azure Database usage. They already have on-premise Windows Server licenses, so they enroll in Software Assurance to get a credit for those licenses with the Azure Hybrid Benefit when deploying their VMs to save on operating costs.

Post-deployment optimization in Azure: After running their environment on Azure for a few months, Contoso wants to review and optimize their workloads. They use Azure Advisor to get personalized and actionable recommendations on how to enhance their cost-effectiveness. Leveraging these recommendations, they purchase Azure savings plan for compute for their dynamic compute workloads that may change regions or scope and right-size their VMs.

Source: microsoft.com

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Streamline workflows with DevOps solutions from GitHub and Azure

DevOps platforms have demonstrated immense importance in accelerating software delivery and fostering collaboration between development and operations teams. In the fast-paced landscape of software development, the culture of DevOps has been widely recognized and adopted as it unites people, processes, and technologies. The joint solution of GitHub and Azure, combined with the power of Visual Studio, can create order for previously staggered workflows and delayed deployments.

Microsoft has been recognized as a Leader by Gartner® and placed furthest for Completeness of Vision on the Magic Quadrant for DevOps platforms. Combined as a single DevOps solution, GitHub and Azure DevOps can address dev productivity challenges like slow coding, compromised quality, and mundane, repeatable tasks.

Microsoft is committed to accelerating developer productivity with its full developer stack

Microsoft offers an additional cloud development environment aside from GitHub Codespaces. Microsoft Dev Box gives developers self-service access to secure, high-performance workstations in the cloud that are preconfigured and ready-to-code for their projects. Dev Box enables developer teams to tailor their workstation to their projects with compute options to support even the most demanding workloads and integrations with tools like Visual Studio to deliver a seamless developer experience.

When an organization adopts GitHub and Azure DevOps, it encompasses more than just a set of tools that work well together. GitHub Enterprise is an enterprise-ready software development platform designed for the complex workflows of modern development. It’s no wonder it’s adopted by 90% of the Fortune 100. Azure DevOps supports a collaborative culture and set of processes that bring together developers, project managers, and contributors to create software. Together, development teams can seamlessly use their GitHub repositories with integrations with a variety of Azure DevOps services.

Organizations can also easily integrate Visual Studio, the most widely used integrated development environment (IDE) on the market, for an even more productive workflow and seamless build.

When procured jointly under one license, your team will experience:

• Smarter collaboration:
• GitHub and Azure DevOps facilitate smarter collaboration among teams by integrating features such as code reviews embedded in every pull request, timeline-style interfaces, integrated issue tracking, and project boards. This fosters a dynamic environment where teams seamlessly collaborate, ensuring a unified and efficient workflow.
• Streamlined security:
• GitHub Enterprise empowers organizations to enforce robust security policies without burdening existing processes. Integration with the organization’s authentication systems and centralized permission management ensures a secure development ecosystem. With GitHub Advanced Security (GHAS), vulnerabilities are identified and rectified proactively, preventing them from reaching production. Plus, GHAS notifies teams if secrets are exposed in the codebase, enhancing the overall security posture.
• Simpler administration:
• GitHub Enterprise offers versatility in deployment options. Whether on in-house servers or private/public cloud infrastructure, the platform provides multiple deployment pathways. This flexibility is complemented by advanced monitoring tools and activity dashboards, offering full visibility into projects and teams. Simpler administration becomes a reality, allowing organizations to manage their development environment with ease and efficiency.
• Increased scalability:
• GitHub Enterprise is engineered with workflow and administration features tailored for scalability to support organizations in their growth. High availability and load balancing features ensure that the platform seamlessly scales alongside your organization’s expanding needs. As the demands increase, GitHub Enterprise easily offers a scalable foundation for success.

What’s next for engineering and development teams

A more recent evolution from DevOps principles is platform engineering. This new approach helps scale DevOps efforts across teams by standardizing development toolsets used across the organization and providing developers with self-service, on-demand access to deploy the resources they need via a secure, centrally managed platform. Platform engineering empowers developers to achieve scale by eliminating toil and reducing cognitive load while making it easier for admins to meet broader operational and organizational standards.

Many organizations have already adopted this mindset and have implemented their own internal platform engineering teams. In fact, Gartner projects that “By 2026, 80% of large software engineering organizations will establish platform engineering teams as internal providers of reusable services, components and tools for application delivery.”

Teams can leverage building blocks from Microsoft and its myriad of tools to create their own personalized, optimized, and secure developer experiences.

Microsoft also leads AI innovation with the most widely adopted AI developer tool—GitHub Copilot. GitHub Copilot now works within Visual Studio, enabling developers to stay in their workflow and complete tasks faster with the help of multi-line suggestions prompted by existing code and code comments. Building new functionality, writing unit tests, and learning new technologies has never been easier, streamlining the software development lifecycle (SDLC) like never before. The most recent feature added to this tool is Copilot Chat, enabling the rise of natural language as the new universal programming language for every developer on the planet.

With each of these platforms—DevOps and platform engineering—and continued innovation in AI through Copilot and Copilot Chat, Microsoft is helping guide development teams to success and foster a culture of continuous improvement and streamlined, satisfying developer experiences. Leveraging the Microsoft portfolio for DevOps workflows and platforms, organizations experience seamless, secure, yet flexible solutions to build next-gen applications where they want, when they want.

As organizations continue to navigate the dynamic landscape of DevOps and platform engineering, Microsoft will remain at the forefront, year over year. To unlock the full potential, download the report and learn more about the power of Microsoft DevOps solutions.

Source: microsoft.com

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Build next-generation, AI-powered applications on Microsoft Azure

The potential of generative AI is much bigger than any of us can imagine today. From healthcare to manufacturing to retail to education, AI is transforming entire industries and fundamentally changing the way we live and work. At the heart of all that innovation are developers, pushing the boundaries of possibility and creating new business and societal value even faster than many thought possible. Trusted by organizations around the world with mission-critical application workloads, Azure is the place where developers can build with generative AI securely, responsibly, and with confidence.

Welcome to Microsoft Build 2023—the event where we celebrate the developer community. This year, we’ll dive deep into the latest technologies across application development and AI that are enabling the next wave of innovation. First, it’s about bringing you state-of-the-art, comprehensive AI capabilities and empowering you with the tools and resources to build with AI securely and responsibly. Second, it’s about giving you the best cloud-native app platform to harness the power of AI in your own business-critical apps. Third, it’s about the AI-assisted developer tooling to help you securely ship the code only you can build.

We’ve made announcements in all key areas to empower you and help your organizations lead in this new era of AI.

Bring your data to life with generative AI

Generative AI has quickly become the generation-defining technology shaping how we search and consume information every day, and it’s been wonderful to see customers across industries embrace Microsoft Azure OpenAI Service. In March, we announced the preview of OpenAI’s GPT-4 in Azure OpenAI Service, making it possible for developers to integrate custom AI-powered experiences directly into their own applications. Today, OpenAI’s GPT-4 is generally available in Azure OpenAI Service, and we’re building on that announcement with several new capabilities you can use to apply generative AI to your data and to orchestrate AI with your own systems.

We’re excited to share our new Azure AI Studio. With just a few clicks, developers can now ground powerful conversational AI models, such as OpenAI’s ChatGPT and GPT-4, on their own data. With Azure OpenAI Service on your data, coming to public preview, and Azure Cognitive Search, employees, customers, and partners can discover information buried in the volumes of data, text, and images using natural language-based app interfaces. Create richer experiences and help users find organization-specific insights, such as inventory levels or healthcare benefits, and more.

To further extend the capabilities of large language models, we are excited to announce that Azure Cognitive Search will power vectors in Azure (in private preview), with the ability to store, index, and deliver search applications over vector embeddings of organizational data including text, images, audio, video, and graphs. Furthermore, support for plugins with Azure OpenAI Service, in private preview, will simplify integrating external data sources and streamline the process of building and consuming APIs. Available plugins include plugins for Azure Cognitive Search, Azure SQL, Azure Cosmos DB, Microsoft Translator, and Bing Search. We are also enabling a Provisioned Throughput Model, which will soon be generally available in limited access to offer dedicated capacity.

Customers are already benefitting from Azure OpenAI Service today, including DocuSign, Volvo, Ikea, Crayon, and 4,500 others.

We continue to innovate across our AI portfolio, including new capabilities in Azure Machine Learning, so developers and data scientists can use the power of generative AI with their data. Foundation models in Azure Machine Learning, now in preview, empower data scientists to fine-tune, evaluate, and deploy open-source models curated by Azure Machine Learning, models from Hugging Face Hub, as well as models from Azure OpenAI Service, all in a unified model catalog. This will provide data scientists with a comprehensive repository of popular models directly within the Azure Machine Learning registry.

We are also excited to announce the upcoming preview of Azure Machine Learning prompt flow that will provide a streamlined experience for prompting, evaluating, tuning, and operationalizing large language models. With prompt flow, you can quickly create prompt workflows that connect to various language models and data sources. This allows for building intelligent applications and assessing the quality of your workflows to choose the best prompt for your case. See all the announcements for Azure Machine Learning.

It’s great to see momentum for machine learning with customers like Swift, a member-owned cooperative that provides a secure global financial messaging network, who is using Azure Machine Learning to develop an anomaly detection model with federated learning techniques, enhancing global financial security without compromising data privacy. We cannot wait to see what our customers build next.

Run and scale AI-powered, intelligent apps on Azure

Azure’s cloud-native platform is the best place to run and scale applications while seamlessly embedding Azure’s native AI services. Azure gives you the choice between control and flexibility, with complete focus on productivity regardless of what option you choose.

Azure Kubernetes Service (AKS) offers you complete control and the quickest way to start developing and deploying intelligent, cloud-native apps in Azure, datacenters, or at the edge with built-in code-to-cloud pipelines and guardrails. We’re excited to share some of the most highly anticipated innovations for AKS that support the scale and criticality of applications running on it.

To give enterprises more control over their environment, we are announcing long-term support for Kubernetes that will enable customers to stay on the same release for two years—twice as long as what’s possible today. We are also excited to share that starting today, Azure Linux is available as a container host operating system platform optimized for AKS. Additionally, we are now enabling Azure customers to access a vibrant ecosystem of first-party and third-party solutions with easy click-through deployments from Azure Marketplace. Lastly, confidential containers are coming soon to AKS, as a first-party supported offering. Aligned with Kata Confidential Containers, this feature enables teams to run their applications in a way that supports zero-trust operator deployments on AKS.

Azure lets you choose from a range of serverless execution environments to build, deploy, and scale dynamically on Azure without the need to manage infrastructure. Azure Container Apps is a fully managed service that enables microservices and containerized applications to run on a serverless platform. We announced, in preview, several new capabilities for teams to simplify serverless application development. Developers can now run Azure Container Apps jobs on demand and schedule applications and event-driven ad hoc tasks to asynchronously execute them to completion. This new capability enables smaller executables within complex jobs to run in parallel, making it easier to run unattended batch jobs right along with your core business logic. With these advancements to our container and serverless products, we are making it seamless and natural to build intelligent cloud-native apps on Azure.

Integrated, AI-based tools to help developers thrive

Making it easier to build intelligent, AI-embedded apps on Azure is just one part of the innovation equation. The other, equally important part is about empowering developers to focus more time on strategic, meaningful work, which means less toiling on tasks like debugging and infrastructure management. We’re making investments in GitHub Copilot, Microsoft Dev Box, and Azure Deployment Environments to simplify processes and increase developer velocity and scale.

GitHub Copilot is the world’s first at-scale AI developer tool, helping millions of developers code up to 55 percent faster. Today, we announced new Copilot experiences built into Visual Studio, eliminating wasted time when getting started with a new project. We’re also announcing several new capabilities for Microsoft Dev Box, including new starter developer images and elevated integration of Visual Studio in Microsoft Dev Box, that accelerates setup time and improves performance. Lastly, we’re announcing the general availability of Azure Deployment Environments and support for HashiCorp Terraform in addition to Azure Resource Manager.

Enable secure and trusted experiences in the era of AI

When it comes to building, deploying, and running intelligent applications, security cannot be an afterthought—developer-first tooling and workflow integration are critical. We’re investing in new features and capabilities to enable you to implement security earlier in your software development lifecycle, find and fix security issues before code is deployed, and pair with tools to deploy trusted containers to Azure.

We’re pleased to announce GitHub Advanced Security for Azure DevOps in preview soon. This new solution provides the three core features of GitHub Advanced Security into the Azure DevOps platform, so you can integrate automated security checks into your workflow. It includes code scanning powered by CodeQL to detect vulnerabilities, secret scanning to prevent the inclusion of sensitive information in code repositories, and dependency scanning to identify vulnerabilities in open-source dependencies and provide update alerts.

While security is at the top of the list for any developer, using AI responsibly is no less important. For almost seven years, we have invested in a cross-company program to ensure our AI systems are responsible by design. Our work on privacy and the General Data Protection Regulation (GDPR) has taught us that policies aren’t enough; we need tools and engineering systems that help make it easy to build with AI responsibly. We’re pleased to announce new products and features to help organizations improve accuracy, safety, fairness, and explainability across the AI development lifecycle.

Azure AI Content Safety, now in preview, enables developers to build safer online environments by detecting and assigning severity scores to unsafe images and text across languages, helping businesses prioritize what content moderators review. It can also be customized to address an organization’s regulations and policies. As part of Microsoft’s commitment to responsible AI, we’re integrating Azure AI Content Safety across our products, including Azure OpenAI Service and Azure Machine Learning, to help users evaluate and moderate content in prompts and generated content.

Additionally, the responsible AI dashboard in Azure Machine Learning now supports text and image data in preview. This means users can more easily identify model errors, understand performance and fairness issues, and provide explanations for a wider range of machine learning model types, including text and image classification and object detection scenarios. In production, users can continue to monitor their model and production data for model and data drift, perform data integrity tests, and make interventions with the help of model monitoring, now in preview.

We are committed to helping developers and machine learning engineers apply AI responsibly, through shared learning, resources, and purpose-built tools and systems.

Let’s write this history, together

AI is a massive shift in computing. Whether it is part of your workflow or part of cloud development, powering your next-generation, intelligent apps, this community of developers is leading this shift. 

We are excited to bring Microsoft Build to you, especially this year as we go deep into the latest AI technologies, connect you with experts from within and outside of Microsoft, and showcase real-world solutions powered by AI.

Source: microsoft.com

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Announcing Azure Firewall enhancements for troubleshooting network performance and traffic visibility

IT security administrators are often called on to troubleshoot network issues. For instance, a critical application may exhibit latency or disconnections, frustrating end users. These issues may be caused by a recent routing update or changes in security. In some cases, the cause may be due to a sudden burst in network traffic—overwhelming the network resources.

Read More: SC-900: Microsoft Security, Compliance, and Identity Fundamentals

Microsoft Azure Firewall now offers new logging and metric enhancements designed to increase visibility and provide more insights into traffic processed by the firewall. IT security administrators may use a combination of the following to root cause application performance issues:

o Latency Probe metric is now in preview.

o Flow Trace Log is now in preview.

o Fat Flows Log is now in preview.

Azure Firewall is a cloud-native firewall as a service offering that enables customers to centrally govern and log all their traffic flows using a DevOps approach. The service supports both application and network-level filtering rules and is integrated with the Microsoft Defender Threat Intelligence feed to filter known malicious IP addresses and domains. Azure Firewall is highly available with built-in auto-scaling.

Latency Probe metric—now in preview

In a network infrastructure, one may observe increases in latency depending on various factors. The ability to monitor the latency of the firewall is essential for proactively engaging in any potential issues with traffic or services in the infrastructure.

The Latency Probe metric is designed to measure the overall latency of Azure Firewall and provide insight into the health of the service. IT administrators can use the metric for monitoring and alerting if there is observable latency and diagnosing if the Azure Firewall is the cause of latency in a network.

In the case that Azure Firewall is experiencing latency, this can be due to various reasons, such as high CPU utilization, traffic throughput, or networking issues. As an important note, this tool is powered by Pingmesh technology, which means that the metric measures the average latency of the firewall itself. The metric does not measure end-to-end latency or the latency of individual packets.

Figure 1: Dashboard view of healthy firewall latency measured by the Latency Probe (Preview) metric.

 

Flow Trace logs—now in preview

Azure Firewall logging provides logs for various traffic—such as network, application, and threat intelligence traffic. Today, these logs show traffic through the firewall in the first attempt at a Transmission Control Protocol (TCP) connection, also known as the SYN packet. However, this fails to show the full journey of the packet in the TCP handshake. The ability to monitor and track every packet through the firewall is paramount for identifying packet drops or asymmetric routes.

To dive further into an asymmetric routing example, Azure Firewall—as a stateful firewall—maintains state connections and automatically and dynamically allows traffic to successfully come back to the firewall. However, asymmetric routing can occur when a packet takes one path to the destination through the firewall and takes a different path when attempting to return to the source. This can be due to user misconfiguration, such as adding an unnecessary route in the path of the firewall.

As a result, one can verify if a packet has successfully flowed through the firewall or if there is asymmetric routing by viewing the additional TCP handshake logs in Flow Trace.

To do so, you can monitor network logs to view the first SYN packet and click "enable Flow Trace" to see the additional flags for verification:

o SYN-ACK

o FIN

o FIN-ACK

o RST

o INVALID

By adding these additional flags in Flow Trace logs, IT administrators can now see the return packet, if there was a failed connection, or an unrecognized packet. To enable these logs, please read the documentation linked below.

Figure 2: Flow Trace logs displaying SYN-ACK and FIN packets.

Top Flows—now in preview

Today, Microsoft Azure Firewall Standard can support up to 30 Gbps and Azure Firewall Premium can support up to 100 Gbps of traffic processing. However, in any case, sometimes traffic flows can either be unintentionally or intentionally “heavy” depending on the size, duration, and other factors of the packets. Since these flows can potentially impact other flows and the processing of the firewall, it’s important to monitor these traffic flows, to ensure that the firewall can perform optimally.

The Top Flows log—or industry-known as Fat Flows—log shows the top connections that are contributing to the highest bandwidth in a given time frame through the firewall.

This visibility provides the following benefits for IT administrators:

o Identifying the top traffic flows traversing through the firewall.

o Identifying any unexpected or anomaly traffic.

o Deciding what traffic should be allowed or denied, based on results and goals.

To enable these logs, please read the documentation linked below.

Figure 3: Top Flow logs displaying traffic with the top flow rates.

Source: microsoft.com

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Agile teams align and get to market faster with Mural and Microsoft

During the last two years navigating changing economic climates and a global pandemic that shifted the way we work, we’ve learned that teams can continue to collaborate together productively and effectively in remote and hybrid settings. A recent Microsoft study shows that hybrid work works. The biggest risk of a hybrid workforce is not unproductive team members—it's that people are becoming burnt out from too many meetings and a lack of clear, defined priorities.

But it takes intention and leadership to design a company culture where collaboration workflows let everyone contribute equally in retrospectives, standups, planning, and other agile projects where bold ideas and clear alignment are critical to staying competitive in the market. Now, more than ever, agile teams, or any team focused on planning and strategizing are expected seamlessly connect and collaborate regardless of where everyone is working.

Microsoft, in partnership with Mural’s leading collaborative intelligence system, is filling a critical need. Powered by Microsoft Azure and integrated with Microsoft Teams, Mural enables dynamic meetings and teamwork turning ideas and insights into reality so remote work no longer inhibits co-creation. Mural's latest integration with the Microsoft ecosystem connects Azure DevOps to an intuitive workspace for teams to collaborate visually. Mural centers around a digital whiteboard and includes pre-built frameworks that guide and structure collaborative work, including templates for agile ceremonies and LUMA design thinking practices. Facilitation tools built into the product, like voting and a timer, make it intuitive for teams to work together intentionally and make it easier for everyone to contribute. Mural’s new integration with Azure DevOps is the connective tissue for agile teams, no matter where they’re working.

Level up your team’s next collaboration session with Mural and Azure DevOps

With Mural’s new integration, Mural and Azure DevOps customers can now save time and jumpstart collaboration by easily importing work items as sticky notes onto a Mural canvas. Teams can use Mural’s visual canvas and templates to refine work items in a backlog, review a sprint plan, break down a feature into smaller chunks of work, have a retrospective, or even build a program board. Having work items on the canvas where collaboration is happening keeps workflows connected and teams focused without incurring a toggle tax by switching between applications.

93 percent of surveyed organizations who leverage agile experienced an increase in collaboration and productivity following the implementation of Mural.

Everything from user story mapping to retrospectives are more productive, inclusive, and engaging when you connect Mural and Azure DevOps.

How Mural and Azure DevOps work

Connect Azure DevOps to your Mural account to view your projects and easily import work items as sticky notes. Use filters or run your own query to find the work items you want to import.

Design your import in Mural by choosing which fields, including custom fields, to import as tags on your sticky. Stickies are automatically color coded by work item type to visualize and organize next steps and prevent delays in decision making.

Be even more effective with Mural’s templates for hybrid agile ceremonies like program implement (PI) planning, sprint planning, sprint reviews, and more to level up collaboration and reduce time to market.

Partners in collaboration transformation

Mural is a Microsoft Partner of the Year award winner, which recognizes partners based on their commitment to customers, the impact of their solutions, and their exemplary use of Microsoft technologies. We are thrilled to collaborate with Mural, an ISV building the next generation of solutions with Azure technology, to help teams everywhere plan smarter, collaborate better, and ship faster. Integration with Azure DevOps improves agility and can optimize your existing cloud investments by helping decrease costs and saving developer time. With comprehensive security and compliance built in, Mural is paving the way for organizations to adopt a DevOps culture. Mural integration with Azure DevOps is truly a “better together” story as both teams recognized early on a need for the integration across both end users, DevOps, and developers. In the early stages of development, both Microsoft and Mural not only helped test the integration, but also incorporated it into their main development lifecycle of Azure DevOps, quickly realizing the full potential of both Mural and Azure DevOps as an all-in-one platform.

“In a hybrid, modern workplace, organizations need to collaborate and brainstorm with virtual teams to develop innovative solutions for their businesses. We’re thrilled to partner with Mural and deliver its latest integration with Azure DevOps to enable teams everywhere to do their best work using a shared collaboration space across platforms.”—Casey McGee, VP of Global ISV Sales and Digital Natives, Microsoft

Source: microsoft.com

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Microsoft named a Leader in The Forrester Wave™: Public Cloud Development and Infrastructure Platforms, 2022

Forrester recently published its report, The Forrester Wave™: Public Cloud Development and Infrastructure Platforms, Global, Q4 2022, placing Microsoft in the “Leaders” category. It’s an honor to be named as one of only two leaders in Forrester’s definitive report on the public cloud development and infrastructure platform market.

The Forrester report recognized Microsoft for its long-term focus on Kubernetes, hybrid, and multicloud capabilities and noted that it is seeking to lead in hybrid and multicloud environments with platform management tools and capabilities. Reference customers praised Microsoft’s service improvements and partnerships. With Microsoft Azure, customers have a trusted cloud partner and the most advanced, highly integrated enterprise IT infrastructure to help them navigate ever-changing environments and achieve business success today, while they build for the future.

Helping developers build any app for any platform

We recognize that developers are the driving engine of innovation. When they are empowered to set up a complete engineering system in seconds, contribute and collaborate with anyone on any device, use the right tool for the job, and integrate with the rest of the organization’s digital estate, organizations can bring innovation to market faster with greater confidence. Azure makes all that possible. For example, with Microsoft Visual Studio, developers can deploy iOS, Android, Windows, Web or embedded apps to wherever they’d like–Azure, hybrid, on-premises and multi-cloud environments. Further, Azure fully supports some of the most popular open source technologies from Linux, to open-sourced databases, to Grafana, allowing organizations to leverage existing investments when running on Azure.

In August, we introduced Microsoft Dev Box, a managed service for developers to create on-demand, high-performance, secure, ready-to-code, project-specific workstations in the cloud, so they can work and innovate anywhere. And we’ve continued to bring new Kubernetes capabilities across Azure, which I’ll cover a little later in this article.

As the range of application development tools continues to grow, we’re seeing a surge in low-code technologies to spur innovation and lower the barrier to entry. Microsoft makes it easy with PowerApps, which provides prebuilt templates, drag-and-drop simplicity, quick deployment and AI-powered assistance, helping anyone create apps using natural language, while enabling the same DevOps practices for low-code tools that customers expect when building trusted enterprise solutions.

In this new world, organizations are harnessing the cloud to create a culture where everyone feels empowered to innovate, while lowering the barrier to creating new types of apps that can take businesses to new heights.

The Microsoft Intelligent Data Platform

We’re entering the age of the “intelligent app,” where every app is AI-enabled and adapts to each organization’s modern data capabilities. However; fragmented digital estates make it difficult for organizations to harness their data to add layers of intelligence to their apps.

At our Build event in May, we announced the Microsoft Intelligent Data Platform that fully integrates databases, analytics, and governance for a unified data estate. With this integration, organizations can power applications at any scale, get actionable insights from all their data, and properly govern data where it resides. To accelerate time to value, customers can use pre-built, customizable, and production-ready AI models as the building blocks for intelligent solutions with Azure Cognitive Services and Azure Applied AI Services.

As AI becomes more mainstream across organizations, it’s essential that employees have the tools to leverage this technology responsibly. We apply Microsoft's Responsible AI Standards to our product development, and have made it a priority to help customers understand, protect, and control their AI solutions with tools and resources like the Responsible AI Dashboard, bot development guidelines, and built-in tools to help explain model behavior, test for fairness and more.

By unifying and integrating data to create more intelligent apps, customers are opening the door to new innovations never thought possible.

From cloud to edge: Innovate securely, anywhere

More and more organizations are embracing hybrid and multicloud as part of their migration and modernization journeys, and they want to continue this flexible approach in a secure, compliant, reliable, and integrated way. Forrester credits Microsoft with "seeking to lead in hybrid and multicloud environments with platform management tools and capabilities, including the Azure Arc management platform."

Azure Arc operates as a bridge extending across the Azure platform by allowing applications and services the flexibility to run across on-premises, edge, and multicloud environments. One of the key challenges organizations face is securing and managing their distributed environments consistently while building innovative applications using cloud-native technologies.

Recently, we announced new deployment options for Azure Kubernetes Services enabled by Azure Arc so customers can run containerized apps, in addition to many first-party Azure application, data, and machine learning services, anywhere regardless of their location.

They can also take advantage of Azure’s comprehensive security, governance, and management capabilities for their Windows, Linux, SQL Server, and Kubernetes deployments in their datacenters, at the edge, or multicloud.

Azure is the only cloud platform built by a security vendor and ensuring that our customers' data is safe and secure is at the forefront of everything we do. For example, our Defender for Cloud security service spans across all clouds—even AWS and Google Cloud for a seamless, consistent, and secure cloud journey where it leads.

Our deep commitment to our customers is baked into every aspect of our vision and roadmap—to be the trusted partner with the most advanced, yet flexible cloud technologies that enable anyone in any organization to innovate anywhere. It’s an honor to be recognized for that commitment and a great way to usher in the New Year.

Source: microsoft.com

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Announcing Azure DNS Private Resolver general availability

A successful hybrid networking strategy demands DNS services that work seamlessly across on-premises and cloud networks. Azure DNS Private Resolver now provides a fully managed recursive resolution and conditional forwarding service for Azure virtual networks. Using this service, you will be able to resolve DNS names hosted in Azure DNS private zones from on-premises networks as well as DNS queries originating from Azure virtual networks that can be forwarded to a specified destination server to resolve them.

This service will provide a highly available and resilient DNS infrastructure on Azure for a fraction of the price of running traditional IaaS VMs running DNS servers in virtual networks. You will be able to seamlessly integrate with Private DNS Zones and unlock key scenarios with minimal operational overhead.

We are excited to share that Azure DNS Private Resolver is now in general availability.

A quick overview of Azure DNS

We offer two types of Azure DNS Zones—private and public—for hosting your private DNS and public DNS records. In the preceding illustration, multi-region workloads running on Azure with Azure DNS Private Resolver are provisioned in two regional, centralized virtual networks with one or more spokes peered to each centralized virtual network. These virtual networks have inbound and outbound endpoints provisioned. From on-premises, there are two distinct locations (East and West) and each location connects via Express Route to the centralized virtual network where Private Resolver is provisioned. These on-premises locations have one or more local DNS servers configured to do conditional forwarding to the inbound endpoint of Private Resolver. The local DNS servers in East have the IP address of the East inbound endpoint as the primary DNS target, and the West inbound endpoint as secondary. Alternatively, the local DNS servers in West have the IP address of the West inbound endpoint as the primary DNS target, and the East inbound endpoint as secondary. There is a single private DNS zone linked to both regions and both on-premises locations can resolve names from this zone even in the event of a regional failure.

◉ Azure Private DNS: Azure Private DNS provides a reliable and secure DNS service for your virtual network. Azure Private DNS manages and resolves domain names in the virtual network without the need to configure a custom DNS solution. By using private DNS zones, you can use your own custom domain name instead of the Azure-provided names during deployment.

◉ Azure Public DNS: DNS domains in Azure DNS are hosted on Azure's global network of DNS name servers. Azure DNS uses anycast networking. Each DNS query is answered by the closest available DNS server to provide fast performance and high availability for your domain.

What is being announced today?

Azure DNS Private Resolver enables you to query Azure DNS private zones from an on-premises environment and vice versa without deploying virtual machine-based DNS servers.

Azure DNS Private Resolver general availability is being announced to all customers and will have regional availability in the following regions:

◉ East US

◉ East US 2

◉ Central US

◉ South Central US

◉ North Central US

◉ West Central US

◉ West US 3

◉ Canada Central

◉ Brazil South

◉ West Europe

◉ North Europe

◉ UK South

◉ France Central

◉ Sweden Central

◉ Switzerland North

◉ East Asia

◉ Southeast Asia

◉ Japan East

◉ Korea Central

◉ South Africa North

◉ Australia East

 

What will customers be able to do with Azure Private Resolver?

Apart from the features which were announced earlier in preview, customers will now be able to leverage the following additional functionality and content:

◉ Additional architectural guidance for higher resiliency and enabling disaster recovery scenarios.

◉ In-depth information on how to configure conditional forwarding rules.

◉ Configuring hybrid name resolution from on-premises.

In the following diagram, an on-premises network connects to Azure via ExpressRoute and has on-premises DNS servers configured to conditionally forward queries to the private IP address of the inbound endpoint. The inbound endpoint then resolves names available on Azure Private DNS zones which are linked to the virtual network where private resolver is provisioned. If there is no matching private DNS zone in the virtual network, it will use the outbound endpoint and resolve using the ruleset rules via longest suffix match. If no match in the ruleset is found it will recurse to the internet for public name resolution.

Features and benefits

◉ Cross-subscription support to link virtual networks from different subscriptions to rulesets.

◉ Resource Health Check Integration to provide visibility of endpoint health to our customers.

◉ Visibility of query metrics per endpoint to plan for future capacity:

◉ PrivateLink enabled services integration in conditional forwarding to exclude Azure infra zones from being resolved on-premises.

Private Resolver general availability is also available to use via PowerShell, CLI, .NET, Java, Python, REST, Typescript, Go, ARM, and Terraform.

Key use cases for this service

◉ Conditionally forward from on-premises with Azure ExpressRoute/VPN and resolve names hosted on Azure Private DNS Zones via private IP address.

◉ Seamlessly resolve Private Endpoints which are registered in Azure Private DNS Zones.

◉ Configure default DNS servers and forward all DNS queries to either a Protective DNS service or other target DNS servers with a wildcard rule.

◉ Conditionally forward to any reachable target DNS server using a simple rule.

◉ Access resources on-premises with Azure Bastion using names hosted on DNS servers on-premises or Azure Private DNS zones.

Fully managed

Built-in high availability, zone redundancy, and low latency name resolution.

Reduces cost

Reduce operating costs and run at a fraction of the price of traditional IaaS solutions.

Private access to your Private DNS Zones

Conditionally forward from your Virtual Networks to any reachable DNS server and from on-premises to Azure Private DNS Zones.

Scalability

High performance per endpoint.

Highly available

Availability Zone aware and resilient to failures within a region. Service-legal agreement (SLA) of 99.99 percent during general availability.

DevOps-friendly

Build your pipelines with Terraform, ARM, or Bicep.

Source: microsoft.com

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Elevate your visualizations with Azure Managed Grafana—now generally available

As part of our continued commitment to open source solutions, we are announcing the general availability of Azure Managed Grafana, a managed service that enables you to run Grafana natively within the Azure cloud platform. With Azure Managed Grafana, you can seamlessly and securely connect with and scale to businesses’ existing Azure services, enhancing observability and cloud management.

In addition to the features announced during preview, with general availability, we’re introducing new capabilities that include the latest Grafana v9.0 features with its improved alerting experience as well as zone redundancy (in preview) and API key support.

New connections and integrations with Azure services

With general availability, we are adding new integrations with Azure services, allowing you to realize the benefits of Grafana as efficiently and effectively as possible.

We have introduced several new out-of-the-box dashboards for Azure Monitor. For example, with Availability Tests Geo Map dashboard for Azure Monitor application insights, you can view the results and responsiveness of your application availability tests based on geographic location. Additionally, with the new out-of-the-box Load Balancing dashboard for Azure Monitor network insights, you can monitor key performance metrics for all your Azure load balancing resources, including Load Balancers, Application Gateway, Front Door, and Traffic Manager.

The new “pin to Grafana” feature for Azure Monitor Logs allows you to seamlessly add charts and queries from Azure Monitor Logs to Grafana dashboards with just one click. In the illustration below, you can see how the Azure Monitor Logs query on the left is replicated in the Grafana interface on the right.

Similarly, we have introduced new out-of-the-box dashboards for Azure Container Apps as well. The new Aggregate View dashboard for Azure Container Apps depicts a geographic map of your container apps filtered by resource group, environment, and region with drill-down links to a detailed dashboard for each app. The new App View dashboard for Azure Container Apps monitors the performance of Azure Container Apps by viewing the key metrics of CPU, memory, restarts, and network traffic or by revision, replica, and status code.

Source: microsoft.com

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MLOps Blog Series Part 3: Testing scalability of secure machine learning systems using MLOps

The capacity of a system to adjust to changes by adding or removing resources to meet demand is known as scalability. Here are some tests to check the scalability of your model.

System testing

System tests are carried out to test the robustness of the design of a system for given inputs and expected outputs (for example, an MLOps pipeline, inference). Acceptance tests (to fulfill user requirements) can be performed as part of system tests.

A/B testing

A/B testing is performed by sending production traffic to alternate systems that will be evaluated. Statistical hypothesis testing is used to decide which system is better.

Figure 1: A/B testing

Canary testing

Canary testing is done by delivering the majority of production traffic to the current system while sending traffic from a small group of users to the new system we're evaluating.

Figure 2: Canary testing

Shadow testing

Sending the same production traffic to various systems is known as shadow testing. Shadow testing is simple to monitor and validates operational consistency.

Figure 3: Shadow testing

Load testing

Load testing is a technique for simulating a real-world load on software, applications, and websites. Load testing simulates numerous users using a software application to simulate the expected usage of the program. It measures the following:

• Endurance: Whether an application can resist the processing load, it is expected to have to endure for an extended period.

• Volume: The application is subjected to a large volume of data to test whether the application performs as expected.

• Stress: Assessing the application's capacity to sustain a specified degree of efficacy in adverse situations.

• Performance: Determining how a system performs in terms of responsiveness and stability under a particular workload.

• Scalability: Measuring the application's ability to scale up or down as a reaction to an increase in the number of users.

Load tests can be performed to test the above factors using various software applications. Let’s look at an example of load testing an AI microservice using locust.io. The dashboard in Figure 4 reflects the total requests made to the microservice per second as well as the response times. Using these insights, we can gauge the performance of the AI microservice under a certain load.

Figure 4: Load testing using Locust.io

Source: microsoft.com

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