Build exciting career opportunities with new Azure skilling options

Microsoft Build is more than just a tech conference—it’s a celebration of innovation, a catalyst for growth, and a gateway to unlocking your professional potential through skilling opportunities on Microsoft Learn. In this blog, we’ll look back at some of the most exciting Microsoft Azure tools that were featured at Build 2024 and put you on the path to attain proficiency.

Unleash the power of AI by mastering intelligent app development 

Azure provides a comprehensive ecosystem of services, tools, and infrastructure tailored for the entire AI lifecycle. At Build we highlighted how your team can efficiently develop, scale, and optimize intelligent solutions that use cutting-edge technologies. 

This year at Build, Microsoft announced the general availability for developers to build and customize models in Microsoft Azure AI Studio. We recently dropped an Azure Enablement Show episode that guides viewers through building their own Copilot using Studio. Watch a demonstration of how to use prompt flow to create a custom Copilot, how to chat with the AI model, and then deploy it as an endpoint. 

Another episode focuses on new Microsoft Azure Cosmos DB developer guides for Node.js and Python, as well as a learning path for building AI chatbots using Azure Cosmos DB and Microsoft Azure Open AI. You’ll learn how to set up, migrate, manage, and utilize V Core-based Azure Cosmos DB for MongoDB to create generative AI apps, culminating in a live demo of an AI chatbot. 

If that Azure Enablement Show episode piques your interest to learn more about Azure Cosmos DB, check out the Microsoft Developers AI Learning Hackathon, where you’ll further explore the world of AI and how to build innovative apps using Azure Cosmos DB, plus get the chance to win prizes! To help you prepare for the hackathon, we have a two-part series to guide you through building AI apps with Azure Cosmos DB, which includes deep-dives into AI fundamentals, Azure Open AI API, vector search, and more.

You can also review our official collection of Azure Cosmos DB learning resources, which includes lessons, technical documentation, and reference sample codes.

Looking for a more structured lesson plan? Our newly launched Plans on Microsoft Learn now provides guided learning for top Azure tools and solutions, including Azure Cosmos DB. Think of it as a structured roadmap for you or your team to acquire new skills, offering focused content, clear milestones, and support to speed up the learning process. Watch for more official Plans on Microsoft Learn over the coming months! 

Empower your developers to achieve improved productivity 

Accelerating developer productivity isn’t just about coding faster; it’s about unlocking innovation, reducing costs, and delivering high-quality software that drives business growth. Azure developer tools and services empowers you to streamline processes, automate workflows, and use advanced technologies like AI and machine learning. 

Join another fun episode of the Azure Enablement Show to discover Microsoft’s skilling resources and tools to help make Python coding more efficient. Learn how to build intelligent apps with Azure’s cloud, AI, and data capabilities and follow along with hands-on modules covering Python web app deployment and machine learning model building on Azure. 

We also have three official collections of learning resources that tackle different aspects of developer productivity:

• Microsoft Developer Tools @ Build 2024: With cutting-edge developer tools and insights, we’ll show you how to create the next generation of modern, intelligent apps. Learn how you can build, test, and deploy apps from the cloud with Microsoft Dev Box, Microsoft Visual Studio, and how Microsoft Azure Load Testing and Microsoft Playwright Testing make it easy to test modern apps.
• Accelerate Developer Productivity with GitHub and Azure for Developers: Continue unlocking the full coding potential in the cloud with GitHub Copilot. Through a series of videos, articles, and activities, you’ll see how GitHub Copilot can assist you and speed up your productivity across a variety of programming languages and projects.
• Secure Developer Platforms with GitHub and Azure: Learn how to elevate your code security with GitHub Advanced Security, an add-on to GitHub Enterprise. Safeguard your private repositories at every development stage with advanced features like secret scanning, code scanning, and dependency management. 

Accelerate your cloud journey with seamless Azure migration

Migrating to Azure empowers organizations to unlock a world of opportunities. At Build we demonstrated how, by using the robust and scalable Azure cloud platform, businesses can modernize their legacy systems, enhance security and compliance, and integrate with AI.

Looking to get more hands-on with Azure migration tools? Check out our lineup of Microsoft Azure Virtual Training Days. These free, two-day, four-hour sessions are packed with practical knowledge and hands-on exercises for in-demand skills.

• Data Fundamentals: In this foundational-level course, you’ll learn core data concepts and skills in Azure cloud data services. Find out the difference between relational and non-relational databases, explore Azure offerings like Azure Cosmos DB, Microsoft Azure Storage, and gain insights into large-scale analytics solutions such as Microsoft Azure Synapse Analytics and Microsoft Azure Databricks.
• Migrate and Secure Windows Server and SQL Server Workloads: This comprehensive look at migrating and securing on-premises Windows Server and SQL Server workloads to Azure offers insights into assessing workloads, selecting appropriate migration options, and using Azure flexibility, scalability, and cost-saving features.

Microsoft Azure SQL is an intelligent, scalable, and secure cloud database service that simplifies your operations and unlocks valuable insights for your business. The curated learning paths in our official Azure SQL collection will enable you to focus on the domain-specific database administration and optimization activities that are critical for your business. 

For an even more structured learning experience, there’s our official Plans on Microsoft Learn offering, Migrate and Modernize with Azure Cloud-Scale Database to Enable AI. Designed to equip you with the expertise needed to harness the full potential of Azure SQL, Microsoft Azure Database for MySQL, Microsoft Azure Database for PostgreSQL, and Microsoft SQL Server enabled by Microsoft Azure Arc for hybrid and multi-cloud environments, this plan will immerse you in the latest capabilities and best practices.

Master cloud-scale data analysis for insightful decision making 

Cloud-scale analytics help businesses gain valuable insights and make data-driven decisions at an unprecedented speed. Our unified analytics platform, Microsoft Fabric, simplifies data integration, enables seamless collaboration, and democratizes access to AI-powered insights, all within a single, integrated environment. 

Looking to take the Fabric Analytics Engineer Associate certification exam? Get ready with Microsoft Fabric Learn Together, a series of live, expert-led sessions designed to help you build proficiency in tools such as Apache Spark and Data Factory and understand concepts from medallion architecture design to lakehouses. 

There’s still time to register for our Virtual Training Day session, Implementing a Data Lakehouse with Microsoft Fabric, which aims to supply data pros with technical experience how to unify data analytics using AI and extract critical insights. Key objectives include identifying Fabric core workloads to deliver insights faster and setting up a data lakehouse foundation for ingestion, transformation, modeling, and visualization.

And of course, don’t miss out on our official collection of learning resources for Microsoft Fabric and Azure Databricks, featuring modules on implementing a Data Lakehouse and using Copilot in Fabric, and workshops on building retrieval augmented generation (RAG) Applications and Azure Cosmos DB for MongoDB vCore. For a more curated experience, our Plans on Microsoft Learn collection will get started on how to ingest data with shortcuts, pipelines, or dataflows, how to transform data with dataflows, procedures, and notebooks, and how to store data in the Lakehouse and Data Warehouse.

Unlock maximum cloud efficiency and savings with Azure

Promoting resiliency on Azure is a strategic approach to managing your cloud resources efficiently, ensuring optimal performance while minimizing costs. By right-sizing virtual machines (VMs), utilizing reserved instances or savings plans, and taking advantage of automation tools like Microsoft Azure Advisor, you can maximize the value of your Azure investment. 

On another fun episode of our Azure Enablement Show, we explore the Learn Live resources available to help you optimize your cloud adoption journey. Confident cloud operations require an understanding of how to manage cost efficiency, reliability, security, and sustainability. Whether you’re an IT pro or just testing the waters, this two-part episode will point you to the learning resources you need. 

Source: microsoft.com

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AI-powered dialogues: Global telecommunications with Azure OpenAI Service

In an era where digital innovation is king, the integration of Microsoft Azure OpenAI Service is cutting through the static of the telecommunications sector. Industry leaders like Windstream, AudioCodes, AT&T, and Vodafone are leveraging AI to better engage with their customers and streamline their operations. These companies are pioneering the use of AI to not only enhance the quality of customer interactions but also to optimize their internal processes—demonstrating a unified vision for a future where digital and human interactions blend seamlessly.

Leveraging Azure OpenAI Service to enhance communication

Below we look at four companies who have strategically adopted Azure OpenAI Service to create more dynamic, efficient, and personalized communication methods for customers and employees alike. 

1. Windstream’s AI-powered transformation streamlines operational efficiencies: Windstream sought to revolutionize its operations, enhancing workflow efficiency and customer service.  

Windstream streamlined workflows and improved service quality by analyzing customer calls and interactions with AI-powered analytics, providing insights into customer sentiments and needs. This approach extends to customer communications, where technical data is transformed into understandable outage notifications, bolstering transparency, and customer trust. Internally, Windstream has capitalized on AI for knowledge management, creating a custom-built generative pre-trained transformer (GPT) platform within Microsoft Azure Kubernetes Service (AKS) to index and make accessible a vast repository of documents, which enhances decision-making and operational efficiency across the company. The adoption of AI has facilitated rapid, self-sufficient onboarding processes, and plans are underway to extend AI benefits to field technicians to provide real-time troubleshooting assistance through an AI-enhanced index of technical documents. Windstream’s strategic focus on AI underscores the company’s commitment to innovation, operational excellence, and superior customer service in the telecommunications environment.

2. AT&T automates for efficiency and connectivity with Azure OpenAI Service: AT&T sought to boost productivity, enhance the work environment, and reduce operational costs. 

AT&T is leveraging Azure OpenAI Service to automate business processes and enhance both employee and customer experiences, aligning with its core purpose of fostering connections across various aspects of life including work, health, education, and entertainment. This strategic integration of Azure and AI technologies into their operations allows the company to streamline IT tasks and swiftly respond to basic human resources inquiries. In its quest to become the premier broadband provider in the United States and make the internet universally accessible, AT&T is committed to driving operational efficiency and better service through technology. The company is employing Azure OpenAI Service for various applications, including assisting IT professionals in managing resources, facilitating the migration of legacy code to modern frameworks to spur developer productivity, and enabling employees to effortlessly complete routine human resources tasks. These initiatives allow AT&T staff to concentrate on more complex and value-added activities, enhancing the quality of customer service. Jeremy Legg, AT&T’s Chief Technology Officer, highlights the significance of automating common tasks with Azure OpenAI Service, noting the potential for substantial time and cost savings in this innovative operational shift. 

3. Vodafone revolutionizes customer service with TOBi and Microsoft Azure AI: Vodafone sought to lower development costs, quickly enter new markets, and improve customer satisfaction with more accurate and personable interactions. 

Vodafone, a global telecommunications giant, has embarked on a digital transformation journey, central to which is the development of TOBi, a digital assistant created using Azure services. TOBi, designed to provide swift and engaging customer support, has been customized and expanded to operate in 15 languages across multiple markets. This move not only accelerates Vodafone’s ability to enter new markets but also significantly lowers development costs and improves customer satisfaction by providing more accurate and personable interactions. The assistant’s success is underpinned by Azure Cognitive Services, which enables it to understand and process natural language, making interactions smooth and intuitive. Furthermore, Vodafone’s initiative to leverage the new conversational language understanding feature from Microsoft demonstrates its forward-thinking approach to providing multilingual support, notably in South Africa, where TOBi will soon support Zulu among other languages. This expansion is not just about broadening the linguistic reach but also about fine-tuning TOBi’s conversational abilities to recognize slang and discern between similar requests, thereby personalizing the customer experience.  

4. AudioCodes leverages Microsoft Azure for enhanced communication: AudioCodes sought streamlined workflows, improved service level agreements (SLAs), and increased visibility. 

AudioCodes, a leader in voice communications solutions for over 30 years, migrated its solutions to Azure for faster deployment, reduced costs, and improved SLAs. The result? The company’s ability to serve its extensive customer base, which includes more than half of the Fortune 100 enterprises. The company’s shift towards managed services and the development of applications aimed at enriching customer experiences is epitomized by AudioCodes Live, designed to facilitate the transition to Microsoft Teams Phone. AudioCodes has embraced cloud technologies, leveraging Azure services to streamline telephony workflows and create advanced applications for superior call handling, such as its Microsoft Teams-native contact center solution, Voca. By utilizing Azure AI and AI, Voca offers enterprises robust customer interaction capabilities, including intelligent call routing and customer relationship management (CRM) integration. AudioCodes’ presence on Azure Marketplace has substantially increased its visibility, generating over 11 million usage hours a month from onboarded customers. The company plans to utilize Azure OpenAI Service in the future to bring generative AI capabilities into its solutions.

The AI enhanced future of global telecommunications 

The dawn of a new era in telecommunications is upon us, with industry pioneers like Windstream, AudioCodes, AT&T, and Vodafone leading the charge into a future where AI and Azure services redefine the essence of connectivity. Their collective journey not only highlights a shared commitment to enhancing customer experience and operational efficiency, but also paints a vivid picture of a world where communication transcends traditional boundaries, enabled by the fusion of cloud infrastructure and advanced AI technologies. This visionary approach is laying the groundwork for a paradigm where global communication is more seamless, intuitive, and impactful, demonstrating the unparalleled potential of AI to weave a more interconnected and efficient fabric of global interaction. 

Our commitment to responsible AI

With responsible AI tools in Azure, Microsoft is empowering organizations to build the next generation of AI apps safely and responsibly. Microsoft has announced the general availability of Azure AI Content Safety, a state-of-the art AI system that helps organizations keep AI-generated content safe and create better online experiences for everyone. Customers—from startup to enterprise—are applying the capabilities of Azure AI Content Safety to social media, education, and employee engagement scenarios to help construct AI systems that operationalize fairness, privacy, security, and other responsible AI principles. 

Source: microsoft.com

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Microsoft open sources Retina: A cloud-native container networking observability platform

The Microsoft Azure Container Networking team is excited to announce Retina, a cloud-native container networking observability platform that enables Kubernetes users, admins, and developers to visualize, observe, debug, and analyze Kubernetes’ workload traffic irrespective of Container Network Interface (CNI), operating system (OS), and cloud. We are excited to release Retina as an open-source repository that helps with DevOps and SecOps related networking cases for your Kubernetes clusters and we invite the open-source community to innovate along with us.

Embracing and advancing open-source software

Cloud native technologies like Kubernetes have made building applications that can run anywhere, easier. At the same time, many applications have become more complex, and managing them in the cloud is increasingly difficult. As companies build cloud-native applications composed of interconnected services and then deploy them to multiple public clouds as well as their private infrastructure, network related observability, troubleshooting, and debugging has become increasingly difficult.

With the power of extended Berkley Packet Filter (eBPF), it is now possible to offer actionable network insights including how containerized micro-services interact and do so in non-intrusive ways without any change in the applications itself—that’s exactly what Retina sets out to achieve. Retina will help democratize network observability and troubleshooting by bringing new focus to the experience of application developers. Retina provides developers with simple ways to observe and troubleshoot their applications for issues such as packet drops and latency without worrying about the complexities of the underlying network infrastructure and transformations.

Based on our positive experience in the community with eBPF and Cilium, we are excited to build on this relationship and engage both more closely and with more communities. We believe that by opening Retina to the community, we can benefit from informed feedback, innovative ideas, and collaborative efforts that will help enhance and expand Retina’s capabilities.

Retina solutions and capabilities

Drawing from our extensive experience managing multiple container networking services for the Azure Kubernetes Service (AKS), we identified critical gaps in network monitoring, the collection of network metrics and traces from Kubernetes clusters. Retina is a cutting-edge solution that closes these gaps and is designed to tackle the complex challenges of managing and supporting Kubernetes networks providing infrastructure- and site-reliability engineers comprehensive insights into cluster networking. Retina also provides deep traffic analysis with Kubernetes-specific context, translating metrics into either industry-standard Prometheus or network flow logs.

Existing open-source solutions are often tightly coupled with specific CNI’s, OS, or data planes, thereby limiting their versatility and use. For this reason, Retina has been designed and developed to be a highly versatile, adaptable, and extensible framework of plugins capable of working seamlessly with any CNI, OS, or cloud provider—making it a valuable addition to any existing toolset. Retina supports both Linux and Windows data planes, ensuring it meets the diverse needs of infrastructure- and site-reliability engineers, while maintaining a minimal memory and CPU footprint on the cluster—this remains true even at scale. Retina’s pluggability design ethos helps us easily extend and adapt to address new use cases without depending on any specific CNI, OS, or data plane.

Figure 1: Architecture overview of Retina

One of Retina’s key features provides deep network traffic insights that include Layer 4 (L4) metrics, Domain Name System (DNS) metrics, and distributed packet captures. It seamlessly integrates the Kubernetes app model offering pod-level metrics with detailed context. It emits actionable networking observability data into industry-standard Prometheus metrics providing node-level metrics (for example, forward, drop, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and Linux utility) and pod-level metrics (such as basic metrics, DNS, and API server latency.)

Retina’s distributed packet captures are label-driven—allowing users to specify what, where, and who to capture packets from. Additionally, it provides historical context of network flow logs and advanced debugging capabilities that enhance network troubleshooting and performance optimization.

Our vision for Retina

Many enterprises are multi-cloud and want solutions that work well not just on Microsoft Azure, but on other clouds as well as on-premises. Retina is open-source and multi-cloud from day one. By open-sourcing Retina, we aim to share our knowledge and vision for Kubernetes networking observability with the broader cloud-native ecosystem. Our hope is that Retina will evolve and grow through collaboration with other developers and organizations who share similar experiences and goals in this field.

In terms of architecture, extensibility was key from the outset and will remain going forward. Retina offers extensibility in data collection—allowing users to easily add new metrics and insights. It also offers extensibility in exporters—enabling users to integrate with other monitoring systems and tools. This flexibility ensures that Retina can adapt to different use cases and environments, making it a versatile and powerful platform for Kubernetes networking observability. In conclusion, we envision Retina as a platform allowing anyone to contribute, extend, and innovate on ultimately creating a robust, purpose-built, and comprehensive solution for Kubernetes networking observability.

Source: microsoft.com

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Modernize and build intelligent apps with support from Microsoft partner solutions

AI transformation drives significant business value, as a recent study of over 2000 business leaders and decision-makers found:

• For every USD1 a company invests in AI, it realizes an average return of USD3.50.
• Organizations realize a return on their AI investments within 14 months.
• 92% of AI deployments take 12 months or less. 
• 71% of respondents say their companies are already using AI.

Clearly, we’re witnessing rapid expansion of AI wherein organizations globally are unlocking productivity within their businesses, but also bending the curve on innovation by building on an open AI platform and partner ecosystem. These organizations are engaging Microsoft experts to build differentiated, intelligent applications and modernize existing, business-critical applications. These intelligent applications use real-time and historical data to deliver personalized and adaptable digital experiences with meaningful outcomes, that close the gap between the user’s current state and the desired outcome. New or modernized, when built on Microsoft Azure, these applications benefit from one of the largest interconnected networks on the planet, high availability, and trusted security and compliance.

Azure brings together capabilities for modern app development, cloud-scale data, and leading generative AI in one place. Customers see great value using these services together. In a recent Forrester Total Economic Impact of Microsoft Azure App Innovation report, customers were able to gain significant time savings of one to one and a half months when delivering new applications to the market, increase developer efficiency up to 25%, and reduce app downtime up to 25%. This leads to compelling business benefits such as beating competitors in the innovation race, capturing incremental revenue, minimizing lost revenue and fines from downtime, and increasing the engagement and retention of key talent.

ISV solutions help accelerate your AI transformation

While Azure provides the tools to build and modernize intelligent applications, it’s important to consider the broader tech stack. Independent Software Vendor (ISV) solutions complement Azure services by allowing you to meet specific use-case requirements, modernize existing tech stacks onto Azure, and mitigate the need to build new skillsets. If your organization routinely uses ISV solutions as part of the app infrastructure or development process, chances are that you can continue to use them even as you build new or modernize existing apps onto Azure. An example is apps built on Azure Spring Apps or Azure Red Hat OpenShift.

1. Azure Spring Apps Enterprise

Azure Spring Apps Enterprise is a fully managed service for the Spring Framework, built in collaboration with VMware. Building upon the Spring Framework and incorporating features from VMware Tanzu, Azure Spring Apps Enterprise helps accelerate development with ready-made, enterprise-conformant templates. Azure Spring Apps Enterprise offers full integration into Azure’s ecosystem and services, including fully managed infrastructure, built-in app lifecycle management, and ease of monitoring for app development and modernization. If you have existing apps in the Spring Framework, you can efficiently modernize them onto Azure while managing costs and enhancing the apps with AI. Here’s how to get started: Migrate Spring Boot applications to Azure Spring Apps.

2. Azure Red Hat OpenShift

Azure Red Hat OpenShift is a turnkey application platform. It is jointly engineered, operated, and supported by Red Hat and Microsoft. With Azure Red Hat OpenShift, you can deploy fully managed Red Hat OpenShift clusters without worrying about building and managing the infrastructure and get ready access to and integration with Azure tools, singular billing, integrated support and access to committed spend, and discount programs. This increases operational efficiency, time to value, and allows developers to refocus on innovation to quickly build, deploy, and scale applications. 

Microsoft also supports pure third-party solutions as part of its ISV ecosystem, to complement native Azure services. While these solutions meet a diverse set of use-cases, ranging from analytics to storage, here’s one that’s likely common to many app development or modernization projects—HashiCorp Terraform.

3. HashiCorp Terraform on Azure

An infrastructure as code tool for provisioning and managing cloud infrastructure, HashiCorp Terraform on Azure allows you to define infrastructure as code with declarative configuration files that can be used to create, manage, and update infrastructure. If your organization currently uses Terraform, developers can use their familiarity with the tool to deploy and manage Azure infrastructure using familiar and consistent syntax and tooling. To support this, HashiCorp offers a library of pre-built modules for Azure services, including Azure AI, Azure Kubernetes Service, and Azure Cosmos DB. And as your developers build new modules, perhaps with GitHub Copilot, those modules can be templatized using HashiCorp Terraform for reuse within your organization, setting up your developer teams for greater productivity and velocity.

Build and modernize apps with Azure and our partner ecosystem

So, as you look through your app infrastructure and decide to modernize your existing apps, any Spring apps or Red Hat OpenShift apps can easily be moved to Azure, with HashiCorp Terraform on Azure to assist. While we have only looked at three solutions in this blog, your preferred vendors are likely part of the Azure ISV ecosystem. Microsoft’s ecosystem of partners also includes partners that specialize in offering services to build custom intelligent apps, with industry-specific experience.

Connect with experts from Azure who will be able to guide you on your app architecture that utilizes the appropriate technology and services— Microsoft or partner—for your needs.

Source: microsoft.com

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Microsoft named a Leader in the 2023 Gartner Magic Quadrant for Container Management

Cloud-native technologies like containers and Kubernetes are the future of application development. That’s why we’re honored to announce that Microsoft has been named a Leader in the 2023 Gartner Magic Quadrant for Container Management. We believe that this recognition validates our end-to-end approach for developing and deploying enterprise-grade, cloud-native apps that run on Azure, in datacenters, or at the edge.

Figure 1. Gartner Magic Quadrant for Container Management. Source: Gartner (September 2023). 

Gartner recognition of Microsoft as a Leader in this Magic Quadrant, we feel, highlights the broad and deep integration of Azure Kubernetes Service (AKS) with other Azure services. Customers tell us that using AKS for container management helps them modernize existing apps in stages, as time and budget permit, and creates a roadmap for new, cloud-native development that takes advantage of Azure scale, security, performance, and cost optimization. Developers rely on autoscaling AKS clusters to meet the most challenging performance demands, while fully managed Azure services free teams from time-consuming infrastructure management tasks.

Customers have diverse environments and they want to run containers anywhere. Our customers run AKS on Azure and in hybrid configurations, using Azure Stack HCI on-premises and Azure Arc to manage it all.

Scaling up means skilling up

Recently, we presented at KubeCon North America 2023 and at Microsoft Ignite, where we introduced Microsoft Copilot for Azure (in preview). This AI-powered assistant makes it easy for developers to get the answers they need and to work more efficiently, including AKS.

Many developers at the conferences told us that the integration support in AKS makes adoption easier as their organizations roll out ambitious digital transformation projects. Even though Kubernetes is designed to manage the complexity of many moving parts, that complexity has a learning curve. Container-related expertise is still limited, as the Gartner report points out.

“As Kubernetes continues to become pervasive, a lot of teams find themselves at different steps of their adoption, skill set, or learning stage”

AKS Principal PM Lead Jorge Palma recently posted. Gartner even cautions enterprises against deploying container management “without deep knowledge of developer requirements.” 

Tools like Copilot for Azure help developers do more with Azure and AKS. Microsoft offers many additional resources to help developers—no matter where they are in the adoption cycle. Here are just a few ideas: 

• If you’re at the blank page stage, get real-world examples and solution ideas from our solution architectures.
• Explore Kubernetes solutions and services in Azure Marketplace, where you can find click-through deployments to the Kubernetes platform and flexible billing models. 
• To get inspired, read how the development team behind Forza Horizon 5 converted services to AKS in about a month—without any prior Kubernetes experience—fueling the biggest first week in Xbox Games Studio history. 
• To boost skills, consider one of the professional learning paths provided by Microsoft Learn, such as Introduction to Kubernetes on Azure or Administer containers in Azure.
• To stay on top of your deployment, review these developer best practices. 

Powering the AI revolution with AKS

Generative AI continues to rocket across the landscape—and it’s often built on top of Kubernetes. Cloud-native and AI are working together to fuel innovation at scale, and AKS is part of this revolution of intelligent apps. Developers can build apps in AKS that consume Azure OpenAI Service as part of the architecture. 

AI applications often come with bigger container images, so AKS recently added artifact streaming. Container images can be streamed directly to the nodes where they’re running a high-performance, on-demand protocol. That means pods are scheduled faster and start running more quickly. 

AI applications also push the limits of scale, making cost management a top priority. Microsoft recently announced that teams can get more visibility and transparency into cluster costs right in the Azure portal. The cost analysis add-on for AKS (in preview) uses OpenCost to break down underlying cluster infrastructure costs into specific Kubernetes units, such as cluster and namespace.

In addition, organizations can run specialized machine learning workloads, like large language models (LLMs), on AKS more cost effectively and with less manual configuration. The new AI toolchain operator, a managed add-on based on Kaito, simplifies the process of hosting and distributing open-source AI models and customized inferencing on AKS clusters. Another option for improving cluster efficiency and costs is to use the new open-source provider for running Karpenter on AKS. 

Microsoft also recently announced support for Kubernetes fleets, enabling platform administrators to manage multiple AKS clusters at scale. Azure Kubernetes Fleet Manager addresses the challenge of staging updates across clusters in a safe and predictable way. 

DevOps makes the wheels go round 

As the Gartner report explains, “the combination of DevOps and container technology can be a powerful enabler for application development agility and speed, making DevOps skills the critical factor to deployment success.” DevOps drives quality and promotes consistency with provisioning and management practices, including continuous integration and continuous deployment (CI/CD).

Yet building distributed applications can still be a complex business, which is why the AKS team continues to look for ways to help streamline this process. For example, Draft for AKS (in preview) helps streamline Kubernetes deployment, and new smart defaults speed up cluster configuration. In June 2023, we added Distributed Application Runtime (Dapr) APIs that help developers write and implement simple, portable, resilient, and secured microservices. To automate builds and deploy them to AKS clusters, Azure Pipelines provides CI/CD. 

Developers using Azure Container Apps will find it even easier to deploy code to the cloud and to run AI workloads. New “code-to-cloud” integrated cloud build productivity helps any developer build and run their apps on Azure Container Apps—no container knowledge required. In addition, the recently released landing zone accelerator provides a valuable reference for builders of cloud-native apps and microservices. And for compute-heavy workloads, like model training and batch inferencing, dedicated GPU workload profiles (in preview) provide the power. 

Protecting everything 

According to Gartner, by 2026, the adoption of CSP-native platforms will propel 75% of container instances to be deployed within public cloud environments, up from 50% in 2023. At KubeCon and Ignite, we heard IT, ops, and cybersecurity experts from around the world share their approach to security in the cloud. At Microsoft, we’re committed to providing our customers with the tools and resources they need to protect everything. For containers, that means security measures all along the pipeline—from development to runtime—and across hybrid and multicloud deployments.

At Ignite 2023, we announced that new multicloud container security is coming soon to Microsoft Defender for Cloud. Defender cloud security posture management (CSPM) will extend its advanced agentless scanning, data-aware security posture, cloud security graph, and attack path analysis capabilities to Google Cloud Platform (GCP), providing a single contextual view of cloud risks across Amazon Web Services (AWS), Azure, GCP, and hybrid environments. 

Security admins will also have better visibility into the state of containerized applications so they can prioritize misconfigurations and exposures in their deployments of Amazon Elastic Kubernetes Service and Google Kubernetes Engine clusters. 

Source: microsoft.com

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Announcing the general availability of Azure CNI Overlay in Azure Kubernetes Service

Today, we are thrilled to announce the general availability of Azure CNI Overlay. This is a big step forward in addressing networking performance and the scaling needs of our customers.

As cloud-native workloads continue to grow, customers are constantly pushing the scale and performance boundaries of our existing networking solutions in Azure Kubernetes Service (AKS). For Instance, the traditional Azure Container Networking Interface (CNI) approaches require planning IP addresses in advance, which could lead to IP address exhaustion as demand grows. In response to this demand, we have developed a new networking solution called “Azure CNI Overlay”.

In this blog post, we will discuss why we needed to create a new solution, the scale it achieves, and how its performance compares to the existing solutions in AKS.

Solving for performance and scale

In AKS, customers have several network plugin options to choose from when creating a cluster. However, each of these options have their own challenges when it comes to large-scale clusters.

The “kubenet” plugin, an existing overlay network solution, is built on Azure route tables and the bridge plugin. Since kubenet (or host IPAM) leverages route tables for cross node communication it was designed for, no more than 400 nodes or 200 nodes in dual stack clusters.

The Azure CNI VNET provides IPs from the virtual network (VNET) address space. This may be difficult to implement as it requires a large, unique, and consecutive Classless Inter-Domain Routing (CIDR) space and customers may not have the available IPs to assign to a cluster.

Bring your Own Container Network Interface (BYOCNI) brings a lot of flexibility to AKS. Customers can use encapsulation—like Virtual Extensible Local Area Network (VXLAN)—to create an overlay network as well. However, the additional encapsulation increases latency and instability as the cluster size increases.

To address these challenges, and to support customers who want to run large clusters with many nodes and pods with no limitations on performance, scale, and IP exhaustion, we have introduced a new solution: Azure CNI Overlay.

Azure CNI Overlay

Azure CNI Overlay assigns IP addresses from the user-defined overlay address space instead of using IP addresses from the VNET. It uses the routing of these private address spaces as a native virtual network feature. This means that cluster nodes do not need to perform any extra encapsulation to make the overlay container network work. This also allows this overlay addressing space to be reused for different AKS clusters even when connected via the same VNET.

When a node joins the AKS cluster, we assign a /24 IP address block (256 IPs) from the Pod CIDR to it. Azure CNI assigns IPs to Pods on that node from the block, and under the hood, VNET maintains a mapping of the Pod CIDR block to the node. This way, when Pod traffic leaves the node, VNET platform knows where to send the traffic. This allows the Pod overlay network to achieve the same performance as native VNET traffic and paves the way to support millions of pods and across thousands of nodes.

Datapath performance comparison

This section sneaks into some of the datapath performance comparisons we have been running against Azure CNI Overlay.

Note: We used the Kubernetes benchmarking tools available at kubernetes/perf-tests for this exercise. Comparison can vary based on multiple factors such as underlining hardware and Node proximity within a datacenter among others. Actual results might vary.

Azure CNI Overlay vs. VXLAN-based Overlay

As mentioned before, the only options for large clusters with many Nodes and many Pods are Azure CNI Overlay and BYO CNI. Here we compare Azure CNI Overlay with VXLAN-based overlay implementation using BYO CNI.

TCP Throughput – Higher is Better (19% gain in TCP Throughput)

Azure CNI Overlay showed a significant performance improvement over VXLAN-based overlay implementation. We found that the overhead of encapsulating CNIs was a significant factor in performance degradation, especially as the cluster grows. In contrast, Azure CNI Overlay’s native Layer 3 implementation of overlay routing eliminated the double-encapsulation resource utilization and showed consistent performance across various cluster sizes. In summary, Azure CNI Overlay is a most viable solution for running production grade workloads in Kubernetes.

Azure CNI Overlay vs. Host Network

This section will cover how pod networking performs against node networking and see how native L3 routing of pod networking helps Azure CNI Overlay implementation.

Azure CNI Overlay and Host Network have similar throughput and CPU usage results, and this reinforces that the Azure CNI Overlay implementation for Pod routing across nodes using the native VNET feature is as efficient as native VNET traffic.

TCP Throughput – Higher is Better (Similar to HostNetwork)

Azure CNI Overlay powered by Cilium: eBPF dataplane

Up to this point, we’ve only taken a look at Azure CNI Overlay benefits alone. However, through a partnership with Isovalent, the next generation of Azure CNI is powered by Cilium. Some of the benefits of this approach include better resource utilization by Cilium’s extended Berkeley Packet Filter (eBPF) dataplane, more efficient intra cluster load balancing, Network Policy enforcement by leveraging eBPF over iptables, and more.

In Azure CNI Overlay Powered by Cilium, Azure CNI Overlay sets up the IP-address management (IPAM) and Pod routing, and Cilium provisions the Service routing and Network Policy programming. In other words, Azure CNI Overlay Powered by Cilium allows us to have the same overlay networking performance gains that we’ve seen thus far in this blog post plus more efficient Service routing and Network Policy implementation.

It’s great to see that Azure CNI Overlay powered by Cilium is able to provide even better performance than Azure CNI Overlay without Cilium. The higher pod to service throughput achieved with the Cilium eBPF dataplane is a promising improvement. The added benefits of increased observability and more efficient network policy implementation are also important for those looking to optimize their AKS clusters.

TCP Throughput – Higher is better

To wrap up, Azure CNI Overlay is now generally available in Azure Kubernetes Service (AKS) and offers significant improvements over other networking options in AKS, with performance comparable to Host Network configurations and support for linearly scaling the cluster. And pairing Azure CNI Overlay with Cilium brings even more performance benefits to your clusters. We are excited to invite you to try Azure CNI Overlay and experience the benefits in your AKS environment.

Source: microsoft.com

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Build resilient applications with Kubernetes on Azure

Welcome to KubeCon EU 2020, the virtual edition. While we won’t be able to see each other in person at KubeCon EU this year, we're excited that this new virtual format of KubeCon will make the conference more accessible than ever, with more people from the amazing Kubernetes community able to join and participate from around the world without leaving their homes.

With everything that has been happening, the last year has been an up and down experience, but through it all I’m incredibly proud of the focus and dedication from the Azure Kubernetes team. They have continued to iterate and improve our Kubernetes on Azure that provides enterprise-grade experience for our customers.

Kubernetes on Azure (and indeed anywhere) delivers an open and portable ecosystem for cloud-native development. In addition to this core promise, we also deliver a unique enterprise-grade experience that ensures the reliability and security your workloads demand, while also enabling the agility and efficiency that business today desires. You can securely deploy any workload to Azure Kubernetes Service (AKS) to drive cost-savings at scale across your business. Today, we're going to tell you about even more capabilities that can help you along on your cloud-native journey to Kubernetes on Azure.

Improving latency and operational efficiency

One of the key drivers of cloud adoption is reducing latency. It used to be that it took days to get physical computers and set them up in a cluster. Today, you can deploy a Kubernetes cluster on Azure in less than five minutes. These improvements benefit the agility of our customers. For customers who want to scale and provision faster, we are announcing a preview of ephemeral OS disk support which makes responding to new compute demands on your cluster even faster.

Latency isn’t just about the length of time to create a cluster. It’s also about how fast you can detect and respond to operational problems. To help enterprises improve their operational efficiency, we’re announcing preview integration with Azure Resource Health which can alert you if your cluster is unhealthy for any reason. We’re also announcing the general availability of node image updates which allow you to upgrade the underlying operating system to respond to bugs or vulnerabilities in your cluster while staying on the same Kubernetes version for stability.

Finally, though Kubernetes has always enabled enterprises to drive cost savings through containerization, the new economic realities of the world during a pandemic mean that achieving cost efficiency for your business is more important than ever. We’ve got a great exercise that can help you learn how to optimize your costs using containers and the Azure Kubernetes Service.

Secure by design with Kubernetes on Azure

One of the key pillars of any enterprise computing platform is security. With market-leading features like policy integration and Azure Active Directory identity for Pods and cloud-native security have always been an important part of the Azure Kubernetes Service. I’m excited about some new features we’ve added recently to further enhance the security of your workloads running on Kubernetes.

Though Kubernetes has built-in support for secrets, most enterprise environments require a more secure and more compliant implementation. In the Azure Kubernetes Service, being enterprise-grade means providing integration between Azure Key Vault and the Azure Kubernetes service. Using Key Vault with Kubernetes enables you to securely store your credentials, certificates, and other secrets in state of the art, compliant secret store, and easily use them with your applications in an Azure Kubernetes cluster.

It’s even more exciting that this integration is built on the back of an open Container Storage Interface (CSI) driver that the Azure team built and open sourced for the entire Kubernetes community. Giving back to open source is an important part of what it means to be a community steward, and it was exciting to see our approach get validated as it was picked up and used by the HashiCorp Vault team for their secrets integration. Our open source team has been hard at work on improving many other parts of the security ecosystem. We’ve enhanced the CSI driver for Windows, and worked on cgroups v2 and containerd. If you want to learn more about how to secure your cloud-native workloads and make sure that your enterprise is following Microsoft’s best practices, check out our guide to Kubernetes best practices. They will teach you how to integrate firewalls, policy, and more to ensure you have both security and agility in your cloud-native development.

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Monitoring Azure Arc enabled Kubernetes and servers

Azure Arc is a preview service that enables users to create and attach Kubernetes clusters both inside and outside of Azure. Azure Arc also enables the user to manage Windows and Linux machines outside of Azure the same way native Azure Virtual Machines are managed. To monitor these Azure Arc enabled clusters and servers, you can use Azure Monitor the same way you would use it for the Azure resources.

With Azure Arc, the Kubernetes clusters and servers are given a full-fledged Azure Resource ID and managed identity, enabling various scenarios that simplifies management and monitoring of these resources from a common control plane. For Kubernetes, this enables scenarios such as deploying applications through GitOps-based management, applying Azure policy, or monitoring your containers. For servers, users also benefit from applying Azure policies and collecting logs with Log Analytics agent for virtual machine (VM) monitoring.

Monitoring Azure and on-premises resources with Azure Monitor

As customers begin their transition to the cloud, monitoring on-premises resources alongside their cloud infrastructure can feel disjointed and cumbersome to manage. With Azure Arc enabled Kubernetes and Servers, Azure Monitor can enable you to monitor your full telemetry across your cloud-native and on-premises resources in a single place. This saves the hassle of having to configure and manage multiple different monitoring services and bridges the disconnect that many people experience when working across multiple environments.

For example, the below view shows the Map experience of Azure Monitor on an Azure Arc enabled server, with the dashed red lines showing failed connections. The graphs on the right side of the map show detailed metrics about the selected connection.

Also, here you can see your data from Azure Kubernetes Services (AKS), Azure Arc, and Azure Red Hat OpenShift side-by-side in Azure Monitor for containers:

Using Azure Monitor for Azure Arc enabled servers

Azure Monitor for VMs is a complete monitoring offering that gives you views and information about the performance of your virtual machines, as well as dependencies your monitored machines may have. It provides an insights view of a single monitored machine, as well as an at-scale view to look at the performance of multiple machines at once.

Azure Arc enabled servers fit right into the existing monitoring view for Azure Virtual Machines, so the monitoring view on an Azure Arc enabled server will look the same as the view of a native Azure Virtual Machines. From within the Azure Arc blade, you can look at your Azure Arc machines and dive into their monitoring, both through the Performance tab, which shows insights about different metrics such as CPU Utilization and the Map tab, which shows dependencies.

In the at-scale monitoring view, your Azure Arc machines are co-mingled with your native Azure Virtual Machines and Virtual Machines Scale Sets to create a single place to view performance information about your machines. The monitoring data shown in these at-scale views will include all VMs, Virtual Machines Scale Sets, and Azure Arc enabled servers that you have onboarded to Azure Monitor.

The Getting Started tab provides an overview of the monitoring status of your machines, broken down by subscription and resource group.

The Performance tab shows trends at scale, as the performance in certain metrics of all the machines in the chosen subscription and resource group. Within the at-scale view, with the provided Type filter, you can drill down any view to show either your native Azure Virtual Machines, native Azure Virtual Machine Scale Sets, or your Azure Arc enabled servers.

Using Azure Monitor for Azure Arc enabled Kubernetes

Azure Monitor for Containers provides numerous monitoring features to create a thorough experience to understand the health and performance for your Azure Arc clusters.

Azure Monitor provides both an at-scale view for all your clusters, ranging from standard AKS, AKS-engine, Azure Red Hat OpenShift, and Azure Arc. Azure Monitor provides important details, such as:

◉ Health statuses (healthy, critical, warning, unknown).

◉ Node count.

◉ Pod count (user and system).

At the resource level for your Azure Arc enabled Kubernetes, there are several key performance indicators for your cluster. Users can toggle the metrics for these charts based on percentile and pin them to their Azure Dashboards.

In the Nodes, Controllers, and Containers tab, data is displayed across various levels of hierarchy with detailed information in the context blade. By clicking on the View in Analytics, you can take a deep dive into the full container logs to analyze and troubleshoot.

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Microsoft Services is now a Kubernetes Certified Service Provider

Modern applications are increasingly built using containers, which are microservices packaged with their dependencies and configurations. For this reason, many companies are either containerizing their existing applications or creating new complex applications that are composed of multiple containers.

As applications grow to span multiple containers deployed across multiple servers, operating them becomes more complex. To manage this complexity, Kubernetes, an open-source software for deploying and managing those containers at scale, provides an open source API that controls how and where those containers will run.

Kubernetes Certified Service Provider

Microsoft Services is now a Kubernetes Certified Service Provider (KCSP). The KCSP program is a pre-qualified tier of vetted service providers who have deep experience helping enterprises successfully adopt Kubernetes. The KCSP partners offer Kubernetes support, consulting, professional services, and training for organizations embarking on their Kubernetes journey.

We have trained hundreds of consultants on Kubernetes, developed a comprehensive service offering around Kubernetes, and successfully delivered Kubernetes engagements to many customers in all industries, all over the world.

Using our global reach and ecosystem, we empower organizations to put innovation into practice to deliver strategic business outcomes, maximize the value of cloud technology, and drive success through continual support.

Microsoft Services is your partner to enable your organization to leverage container capabilities and frameworks, such as Kubernetes, to adopt modern technologies to increase speed and agility while also maintaining control and good governance.

The Azure Workloads for Containers offering

We recognize a need to help you address your secure infrastructure challenges and requirements. We envision the containers infrastructure to be more than just the containers orchestration layer to include networking, storage, secrets, and Infrastructure as Code (IaC).

Microsoft Services has a full Kubernetes offering, called Azure Workloads for Containers. This offering is composed of several workstreams that focus on the activities and outcomes that are most relevant to our customers. These workstreams provide full flexibility to our customers as each one of them can be selected independently and customized to meet the specific needs of a given project.

Below are the details of these workstreams.

Kubernetes foundation

◉ Design and plan Azure Kubernetes Service (AKS) cluster and shared services.

◉ Implement AKS cluster and shared services.

◉ Deploy application on AKS.

◉ Test application.

◉ Rollout to production. ​

Containers migration

◉ Assess, design, and plan migration.

◉ Migrate the containers-based application(s).

◉ Test the migrated application(s).

◉ Rollout to production.

Kubernetes security hardening

◉ Refactor your security controls for AKS.

◉ Secure your CI/CD pipeline (DevSecOps).

◉ Harden your AKS environment to meet your compliance obligations.

◉ Assist with third-party security product integration.

Kubernetes threat modeling

◉ Build a threat mo​​del based on the AKS cluster and the apps running on it.

◉ Identify threats and mitigations.

◉ Produce clear actions to mitigate the threats.

Application containerization

◉ Create container image(s) for one or multiple applications.

◉ Test the application(s) running as container.

◉ Deploy the application to an AKS cluster in production​.

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Digital transformation with legacy systems simplified

Intelligent insurance means improving operations, enabling revenue growth, and creating engaging experiences—which is the result of digital transformation. The cloud has arrived with an array of technical capabilities that can equip an existing business to move into the future. However insurance carriers face a harder road to transform business processes and IT infrastructures. Traditional policy and claim management solutions lack both cloud-era agility, and the modularity required to react quickly to market forces. And legacy systems cannot be decommissioned unless new systems are fully operational and tested, meaning some overlap between old and new.

The Azure platform offers a wealth of services for partners to enhance, extend, and build industry solutions. Here we describe how one Microsoft partner uses Azure to solve a unique problem.

The need for efficient automation

The prevailing approach to upgrading enterprise software is to engage in large scale IT projects that may take years and significant cost to execute. Delaying may only increase the costs, especially with the burden of continuing (and increasing) compliance. But more importantly, delay results in a significant opportunity cost. Due to competition, insurers are under pressure to pursue lower costs overall, and especially in claim handling. New insurance technology also forces the need for new distribution models and to automate internal workflows and supply chains.

A platform built for transformation

The name of Codafication’s solution is Unity (not to be confused with the Unity game engine platform). Codafication calls Unity an ecosystem Platform-as-a-Service (ePaaS). It enables insurance carriers to accelerate their digital transformation through secure, bi-directional data integration with core and legacy systems. At the same time, the platform enables Codafication’s subscribers to use new cloud-native apps and services. The increase in connectivity means customers, staff and supply chains can integrate more easily and with greater efficiencies.

Unity seeks to address the changing expectations of insured customers without disruption to core policy and claim management functions within the enterprise. Codafication stresses a modular approach to implementing Unity. Their website provides a catalog of components such as project management, supply chain and resource planning, and financial control (and more).

In this graphic, potential inputs for the system include a wide variety of processes, from legacy core systems (expected) to robotic processes (a surprise). The output is equally versatile—dashboards and portals along with data lake and IoT workflow apps.

Insurers can take an iterative and modular approach to solving high value challenges rapidly. Unity provides all the tools required to accelerate digital transformation. Other noteworthy features include:

◈ Custom extensions: use any programming language supported by Docker, in combination with Unity SDKs, to build custom frontend and backend solutions.

◈ Off-the-shelf apps: plug in applications and services (from Codafication) designed for the insurance industry.

◈ Scalability: cloud-native technology, underpinned by Kubernetes, can be hosted in the cloud or in a multi-cloud scenario, with a mix of Docker, serverless and on-premises options.

◈ GraphQL API: leverage the power of a graph database to unlock data silos and find relationships between data stores from legacy systems. Integrate with cloud vendors, AI services and best-in-breed services through a single, secure, scalable and dynamic API.

◈ Integrative technologies: create powerful custom IoT workflows with logic hooks, web hooks and real-time data subscriptions.

Benefits

◈ Through Unity, organizations can interconnect everything and relate data on the fly. Developers can leverage legacy core systems, middleware, and robotics using a microservice architecture driven by a powerful service mesh and extensible framework.

◈ Teams can leverage this infrastructure to deliver (in parallel) solutions into the platform and into the hands of their users. Insurance carriers will find new use cases (like data science uses, and AI) and develop apps rapidly, to deliver projects faster, for less cost and less risk.

◈ Projects can be secured and reused across the infrastructure. This accelerates digital transformation projects without disrupting existing architecture and is the primary step to implementing modern cloud native technologies, such as AI and IoT.

◈ The ‘modernize now, decommission later’ approach to core legacy systems lets an insurer compete and remain relevant against competitors while providing a longer runway for decommissioning aging legacy systems.

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