FSI Knowledge Mining and Intelligent Document Process Reference Architecture
FSI customers such as insurance companies and banks rely on their vast amounts of data to provide sometimes hundreds of individual products to their customers. From assessing product suitability, underwriting, fraud investigations, and claims handling, many employees and applications depend on accessing this data to do their jobs efficiently. Since the capabilities of GenAI have been realised, we have been helping our customers in this market transform their business with unified systems that simplify access to this data and speed up the processing times of these core tasks, while remaining compliant with the numerous regulations that govern the FSI space. Combining the use of Knowledge Mining with Intelligent Document processing provides a powerful solution to reduce the manual effort and inefficacies of ensuring data integrity and retrieval across the many use cases that most of our customers face daily. What is Knowledge Mining and Intelligent Document Processing? Knowledge Mining is a process that transforms large, unstructured data sets into searchable knowledge stores. Traditional search methods often rely on keyword matching, which can miss the context of the information. In contrast, knowledge mining uses advanced techniques like natural language processing (NLP) to understand the context and meaning behind the data, providing a robust searching mechanism that can look across all these data sources, understand the relationships between the data therefore providing more accurate and relevant results. Intelligent Document Processing (IDP) is a workflow automation technology designed to scan, read, extract, categorise, and organise meaningful information from large streams of data. Its primary function is to extract valuable information from extensive data sets without human input, thereby increasing processing speed and accuracy while reducing costs. By leveraging a combination of Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), and Natural Language Processing (NLP), IDP handles both structured and unstructured documents. By ensuring that the processed data meets the "gold standard" - structured, complete, and compliant - IDP helps organizations maintain high-quality, reliable, and actionable data. The Power of Knowledge Mining and Intelligent Document Processing as a Unified Solution Knowledge Mining excels at quickly responding to natural language queries, providing valuable insights and making previously unsearchable data accessible. At the same time, IDP ensures that the processed data meets the "gold standard"—structured, complete, and compliant—making it both reliable and actionable. Together, these technologies empower organisations to harness the full potential of their data, driving better decision-making and improved efficiency. __________________________________________________________________ Meet Alex: A Day in the Life of a Fraud Case Worker Responsibilities: Investigate potential fraud cases by manually searching across multiple systems. Read and analyse large volumes of information to filter out relevant data. Ensure compliance with regulatory requirements and maintain data accuracy. Prepare detailed reports on findings and recommendations. Lost in Data: The Struggles of Manual Fraud Investigation Alex receives a new fraud case and starts by manually searching through multiple systems to gather information. This process takes several hours, and Alex has to read through numerous documents and emails to filter out relevant data. The inconsistent data formats and locations make it challenging to ensure accuracy. By the end of the day, Alex is exhausted and has only made limited progress on the case. Effortless Efficiency: Fraud Investigation Transformed with Knowledge Mining and IDP Alex receives a new fraud case and needs to gather all relevant information quickly. Instead of manually searching through multiple systems, Alex inputs the following natural language query into the unified system: "Show me all documents, emails, and notes related to the recent transactions of client X that might indicate fraudulent activity." The system quickly retrieves and presents a comprehensive summary of all relevant documents, emails, and notes, ensuring that the data is structured, complete, and compliant. This allows Alex to focus on analysing the data and making informed decisions, significantly improving the efficiency and accuracy of the investigation. How has Knowledge Mining and IDP transformed Alex's role? Before implementing Knowledge Mining and Intelligent Document Processing, Alex faced a manual process of searching across multiple systems to gather information. This was time-consuming and labour-intensive, often leading to delays in investigations. The overwhelming volume of data from various sources made it difficult to filter out relevant information, and the inconsistent data formats and locations increased the risk of errors. This high workload not only reduced Alex's efficiency but also led to burnout and decreased job satisfaction. However, with the introduction of a unified system powered by Knowledge Mining and IDP, these challenges were significantly mitigated. Automated searches using natural language queries allowed Alex to quickly find relevant information, while IDP ensured that the data processed was structured, complete, and compliant. This unified system provided a comprehensive view of the data, enabling Alex to make more informed decisions and focus on higher-value tasks, ultimately improving productivity and job satisfaction. ____________________________________________________________________ Example Architecture Knowledge Mining Users can interact with the system through a portal on the customer’s front-end of choice. This will serve as the entry point for submitting queries and accessing the knowledge mining service. Front-end options could include web apps, container services or serverless integrations. Azure AI Search provides powerful RAG capabilities. Meanwhile, Azure Open AI provides access to large language models to summarise responses. These services combined will take the user’s query to search the knowledge base and return relevant information which can be augmented as required. Prompt engineering can provide customisation to how the data is returned. You define what the data sources your Azure AI Search will consume. This can be Azure storage services or other data repositories. Data that meets a pre-defined gold standard is queried by Azure AI Search and relevant data is returned to the user. Gold standard data could be based on compliance or business needs. Power BI can be used to create analytical reports based on the data retrieved and processed. This step involves visualising the data in an interactive and user-friendly manner, allowing users to gain insights and make data-driven decisions. Intelligent Document Processing (Optional) Azure Data Factory is a data integration service that allows you to create workflows for data movement and transforming data at scale. This business data can be easily ingested to your Azure data storage solutions using pre-built connectors. This event driven approach ensures that as new data is generated, it can automatically be processed and ready for use in your knowledge mining solution. Data can be transformed using Functions apps and Azure OpenAI. Through prompt engineering, the large language model (LLM) can highlight specific issues in the documents, such as grammatical errors, irrelevant content, or incomplete information. The LLM can then be used to rewrite text to improve clarity and accuracy, add missing information, or reformat content to adhere to guidelines. Transformed data is stored as gold standard data. ____________________________________________________________________ Additional Cloud Considerations Networking VNETs (Virtual Networks) are a fundamental component of cloud infrastructure that enable secure and isolated networking configurations within a cloud environment. They allow different resources, such as virtual machines, databases, and services, to communicate with each other securely. Virtual networks ensure that services such as Azure AI Search, Azure OpenAI, and Power BI, can securely communicate with each other. This is crucial for maintaining the integrity and confidentiality of sensitive financial data. Express Route or VPN are expected to be used when connecting on-premises infrastructure to Azure for several reasons. Your company Azure ExpressRoute provides a private, reliable, and high-speed connection between your data center and Microsoft Azure. It allows you to extend your infrastructure to Azure by providing private access to resources deployed in Azure Virtual Networks and public services like App service, private end points to various other services. This private peering ensures that your traffic never enters the public Internet, enhancing security and performance. ExpressRoute uses Border Gateway Protocol (BGP) for dynamic routing between your on-premises networks and Azure, ensuring efficient and secure data exchange. It also offers built-in redundancy and high availability, making it a robust solution for critical workloads. Azure Front Door is a cloud-based Content Delivery Network (CDN) and application delivery service provided by Microsoft. It offers several key features, including global load balancing, dynamic site acceleration, SSL offloading, and a web application firewall, making it an ideal solution for optimizing and protecting web applications. We are expecting to use Front door in scenarios when the architecture will be expected to be used by users outside the organisation. Azure API Management in this scenario is expected to be used when we look to rollout the solution to larger groups. We look to then integrate much more security, rate limiting, load balancing, etc. Monitoring and Governance Azure Monitor: This service collects and analyses telemetry data from various resources, providing insights into the performance and health of the system. It enables proactive identification and resolution of issues, ensuring the system runs smoothly. Azure Cost Management and Billing: Provides tools for monitoring and controlling costs associated with the solution. It offers insights into spending patterns and resource usage, enabling efficient financial governance. Application Insights: Provides application performance monitoring (APM) designed to help you understand how your applications are performing and to identify issues that may affect their performance and reliability These components together ensure that the Knowledge Mining and Intelligent Document Processing solution is monitored for performance, secured against threats, compliant with regulations, and managed efficiently from a cost perspective. ____________________________________________________________________ Next steps: Identify the data and its sources that will feed into your own Knowledge Mine. Consider if you also need to implement Intelligent Document Processing to ensure data quality. Define your 'gold standards'. These guidelines will determine how your data might be transformed. Consider how to provide access to the data through an application portal, choose the right front-end technology for your use case. Once you have configured Azure AI search to point to the chosen data, consider how you might augment responses using Azure AI LLM models. Useful resources AI Landing Zone reference architecture Azure and Open AI with API Manager Secure connectivity from on premesis to Azure hosted solutionsAzure Kubernetes Fleet Manager Demo with Terraform Code
Introduction Azure Kubernetes Fleet Manager (Fleet Manager) simplifies the at-scale management of multiple Azure Kubernetes Service (AKS) clusters by treating them as a coordinated “fleet.” One Fleet Manager hub can manage up to 100 AKS clusters in a single Azure AD tenant and region scope, so you can register, organize, and operate a large number of clusters from a single control plane. In this walkthrough, we’ll explore: The key benefits and considerations of using Fleet Manager A real-world e-commerce use case How to deploy a Fleet Manager hub, AKS clusters, and Azure Front Door with Terraform How everything looks and works in the Azure portal Along the way, you’ll see screenshots from my demo environment to illustrate each feature. Why Use Fleet Manager? Managing dozens or even hundreds of AKS clusters individually quickly become unmanageable. Fleet Manager introduces: Centralized control plane: Register AKS clusters across subscriptions/regions under one fleet. Orchestrated upgrades: Define update runs, stages, and groups (ring-based rollouts). Resource propagation: Declaratively push Kubernetes objects (Namespaces, RBAC, ConfigMaps) from hub → members. Cross-cluster L4 load balancing (preview): Distribute TCP/UDP traffic across clusters for high availability. Auto-upgrade profiles: Automatically keep clusters up to date with minimal manual effort. Portal Walkthrough: Exploring Your Fleet Once your Fleet Manager hub and member clusters are up, here’s how it looks in the Azure portal. Member Clusters The Member clusters blade shows all onboarded clusters, their membership status, update group assignment, and Kubernetes version. Figure: Four clusters (two dev, two prod) successfully joined to the fleet, all running version 1.32.3. Multi-Cluster Update Under multi-cluster update, you can manage both Auto-upgrade profiles and Strategies. Auto-upgrade profiles let you enable continuous updates by channel (e.g., Stable) and node image: Strategies define how clusters are grouped and staged during an update run: Figure: We’ve created development-auto-upgrade and production-auto-upgrade profiles, along with matching strategies. Fleet Overview Back on the hub’s Overview blade, you get at-a-glance insights: total member clusters, Kubernetes versions spread, and node image versions. Figure: The hub reports 4 member clusters (all on 1.32.3), and the node pools all share the same image version. Azure Front Door Origin Groups To demonstrate multi-cluster traffic routing, our Terraform deploy includes an Azure Front Door profile with two origin groups (dev & prod). Here’s the Origin groups blade: And the Front Door Overview, showing the endpoint hostname and associated origin groups: Figure: Front Door is configured to route /dev/* to the dev clusters and /prod/* to the prod clusters via these origin groups. Benefits & Considerations Benefits One pane of glass for up to 100 AKS clusters. Ring-based upgrades minimize risk with staged rollouts. Declarative propagation of configs and policies. Global traffic distribution at TCP/UDP (L4) level. Extensible roadmap: Arc support, region failover, Terraform enhancements. Considerations Hub is management-only: No user workloads on the hub. 100-cluster limit per fleet. Regional scope: Hub deployed in one region, though it can manage clusters anywhere. Private hub networking: Private AKS hub requires VNet/jumpbox connectivity. Preview features: Multi-cluster L4 load balancing and Terraform support for update groups are still in preview. Real-World Use Case: Global E-Commerce A multinational retailer runs dev & prod AKS clusters in North America and Europe. They needed: Consistent feature flags & RBAC across clusters Safe, staged upgrades (dev → prod) High-availability checkout traffic routed to healthy clusters Solution with Fleet Manager: Onboard all four clusters into one fleet. Propagate feature-toggle ConfigMaps and RBAC from hub to members. Define update strategies for dev and prod, then run upgrades via CLI or portal. Use Azure Front Door for global routing, failing over between regions. They cut upgrade windows by 60%, eliminated manual sync tasks, and boosted resilience. Reference Architecture for Demo: Deployment with Terraform All of the above is automated in the aks-fleet-manager GitHub repo. Here’s a quick start: 1. Clone repo git clone https://github.com/saswatmohanty01/aks-fleet-manager.git cd aks-fleet-manager/terraform 2. Install CLI tools chmod +x ../scripts/setup-cli.sh ../scripts/setup-cli.sh 3. Authenticate & select subscription az login az account set -s <subscription-id> 4. Initialize Terraform terraform init 5. Configure variables (terraform.tfvars): primary_region = "eastus" secondary_region = "westeurope" resource_prefix = "mycompany" dev_node_count = 2 prod_node_count = 3 6. Plan & apply terraform plan -out=tfplan terraform apply tfplan 7. Create update groups (post-deploy) cd ../scripts chmod +x create-update-groups.sh ./create-update-groups.sh Once complete (about 10–15 minutes), you’ll have: 4 AKS clusters (dev/prod in two regions) A Fleet Manager hub with 4 member clusters Auto-upgrade profiles and strategies An Azure Front Door endpoint routing /dev/ and /prod/ Known Issue. Manual Step in Azure Front Door Refer GitHub: README.md Get the terraform output for all four AKS clusters service endpoint IP addresses. You can get it from step 3 using kubectl get svc for all four clusters. There is a bug, which does not allow to update the service IP addresses for each AKS cluster in Azure Frontdoor->Origin Groups Manually update the IP addresses for Dev and Prod AKS cluster service IP addresses. Go to Azure portal->Azure Front door->Settings->Origin Groups->dev-origin-group Manually update the IP addresses for Dev and Prod AKS cluster service IP addresses. Go to Azure portal->Azure Front door->Settings->Origin Groups->prod-origin-group VS Code Experience Follow the VsCode Deployment Guide from GitHub Repo Conclusion & Next Steps Azure Kubernetes Fleet Manager reduces the pain of managing multi-cluster AKS environments by centralizing control, orchestrating upgrades, and enabling global traffic patterns. To go further: Experiment with auto-upgrade profiles to automate patch deployments. Integrate Fleet operations into CI/CD pipelines with az fleet CLI or Terraform (as features mature). Explore GitOps workflows (Flux/Argo CD) for multi-cluster app deployments. Fleet Manager is evolving rapidly—keep an eye on the preview features and Terraform provider updates. With Fleet Manager, managing up to 100 AKS clusters doesn’t have to be a headache. Give it a try and share your experiences! References Azure Kubernetes Fleet Manager overview (Microsoft Learn) QuickStart: Create a fleet and join member clusters (Microsoft Learn) Fleet Manager CLI commands (Azure CLI docs) aks-fleet-manager GitHub repo & docs Architecture diagram: architecture-diagrams Happy clustering!Announcing GA for Azure Container Apps Serverless GPUs
Azure Container Apps Serverless GPUs accelerated by NVIDIA are now generally available. Serverless GPUs enable you to seamlessly run AI workloads with per-second billing and scale down to zero when not in use. Thus, reducing operational overhead to support easy real-time custom model inferencing and other GPU-accelerated workloads. Serverless GPUs accelerate the speed of AI development teams by allowing customers to focus on core AI code and less on managing infrastructure when using GPUs. This provides an excellent middle layer option between Azure AI Model Catalog's serverless APIs and hosting custom models on managed compute. Now customers can build their own serverless API endpoints for inferencing AI models including custom models. Customers can also provision on-demand GPU-powered Jupyter Notebooks or run other compute-intensive AI workloads that are ephemeral in nature. It provides full data governance as customer’s data never leaves the boundaries of the container while still providing a managed, serverless platform from which to build your applications. This GA release of Serverless GPUs also adds support for NVIDIA NIM microservices, NVIDIA NIM™, part of NVIDIA AI Enterprise, is a set of easy-to-use microservices designed for secure, reliable deployment of high-performance AI model inferencing at scale. Supporting a wide range of AI models, including open-source community and NVIDIA AI Foundation models, NVIDIA NIM ensures seamless, scalable AI inferencing leveraging industry-standard APIs. Key benefits of serverless GPUs Scale-to zero GPUs: Support for serverless scaling of NVIDIA A100 and T4 GPUs. Per-second billing: Pay only for the GPU compute you use. Built-in data governance: Your data never leaves the container boundary. Flexible compute options: Choose between NVIDIA A100 and T4 GPUs. Middle-layer for AI development: Bring your own model on a managed, serverless compute platform and easily run your AI applications alongside your existing apps. Scenarios Our customers have been running a wide range of workloads on serverless GPUs. Below are some common use cases. NVIDIA T4 Real-time and batch inferencing: Using custom open-source models with fast startup times, automatic scaling, and a per-second billing model, serverless GPUs are ideal for dynamic applications that don't already have a serverless API in the model catalog. NVIDIA A100 Compute intensive machine learning scenarios: Significantly speed up applications that implement fine-tuned custom generative AI models, deep learning, or neural networks. High performance computing (HPC) and data analytics: Applications that require complex calculations or simulations, such as scientific computing and financial modeling as well as accelerated data processing and analysis among massive datasets. Serverless GPUs with NVIDIA NIM Serverless GPUs now support NVIDIA NIM microservices, which simplify and accelerate the development of AI applications and agentic AI workflows with pre-packaged, scalable, and performance-tuned models that can be deployed as secure inference endpoints on Azure Container Apps. In order to leverage the power of NVIDIA’s NIM, go to NVIDIA’s API catalog: Try NVIDIA NIM APIs, and select the NIM you wish to run with the ‘Run Anywhere’ NIM type. You will need to set your NGC_API_KEY as an environment variable when deploying Azure Container Apps. For a full set of instructions on how to add a NIM to your container app, follow the instructions here. (Note: Each NIM model has certain hardware requirements, Azure Container Apps serverless GPUs support A100 and T4 GPUs. Please ensure the NIM you are selecting is supported by the hardware.) Quota changes for GA With GA, we are introducing default GPU quotas for enterprise and pay-as-you-go customers. All enterprise agreement customers will have quota for A100 and T4 GPUs. The feature is supported in West US 3, Australia East, and Sweden Central. Get started with serverless GPUs From the portal, you can select to enable GPUs for your Consumption app in the container tab when creating your Container App or your Container App Job. Note: In order to achieve the best performance with serverless GPUs, use an Azure Container Registry (ACR) with artifact streaming enabled for your image tag. Follow steps here to enable artifact streaming on your ACR. To learn more about getting started with serverless GPUs, see our quickstart. You can also add a new consumption GPU workload profile to your existing Container App environment through the workload profiles UX in portal or through the CLI commands for managing workload profiles. Learn more about serverless GPUs and NIMs With serverless GPUs, Azure Container Apps now simplifies the development of your AI applications by providing scale-to-zero compute, pay-as you go pricing, reduced infrastructure management, and more. To learn more, visit: Using serverless GPUs in Azure Container Apps (preview) | Microsoft Learn Tutorial: Generate images using serverless GPUs in Azure Container Apps (preview) | Microsoft Learn Tutorial: Deploy an NVIDIA Llama3 NIM to Azure Container Apps Try NVIDIA NIM APIsGet Ready for .NET Conf: Focus on Modernization
We’re excited to announce the topics and speakers for .NET Conf: Focus on Modernization, our latest virtual event on April 22-23, 2025! This event features live sessions from .NET and cloud computing experts, providing attendees with the latest insights into modernizing .NET applications, including technical upgrades, cloud migration, and tooling advancements. To get ready, visit the .NET Conf: Focus on Modernization home page and click Add to Calendar so you can save the date on your calendar. From this page, on the day of the event you’ll be able to join a live stream on YouTube and Twitch. We will also make the source code for the demos available on GitHub and the on-demand replays will be available on our YouTube channel. Learn more: https://focus.dotnetconf.net/ Why attend? In the fast-changing technological environment we now find ourselves, it has never been more urgent to modernize enterprise .NET applications to maintain competitiveness and stay ahead of the next innovation. Updating .NET applications for the cloud is a major business priority and involves not only technical upgrades and cloud migration, but also improvements in tooling, processes, and skills. At this event, you will get the end to end insights across latest tools, innovations, and best practices for successful .NET modernization. What can developers expect? The event will run live for up to five hours each day, covering different aspects of .NET modernizations. Scott Hanselman will set the tone for day one with discussion of the experiences and processes to modernize .NET applications in the era of AI. This will be followed by expert sessions on upgrading .NET apps and modernizing both your apps and data to the cloud. Day two will soar higher into the clouds, with sessions to help with cloud migration, cloud development, and infusing AI into your apps. You can interact with experts and ask questions to deepen your expertise, as we broadcast live on YouTube, or Twitch. Recordings of all sessions will be available with materials after the event. Agenda Here’s a quick snapshot of the schedule. Things may change, and we recommend that you please visit the event home page for the latest agenda and session times: https://focus.dotnetconf.net/agenda Day 1 – April 22, Tuesday Time (PDT) Session 8:00 am Modernizing .NET: Future-ready applications in the era of AI Scott Hanselman, Chet Husk, McKenna Barlow 9:00 am Deep dive into the upcoming AI-assisted tooling to upgrade .NET apps Chet Husk, McKenna Barlow 10:00 am Use Reliable Web App patterns to confidently replatform your web apps Pablo Lopes 11:00 am Modernize Data-Driven Apps (No AI Needed) Jerry Nixon 12:00 pm Modernize from ASP.NET to ASP.NET Core: The Future is Now Taylor Southwick Day 2 – April 23, Wednesday Time (PDT) Session 8:00 am Unblock .NET modernization with AI-assisted app and code assessment tools Michael Yen-Chi Ho 9:00 am Cloud development doesn't have to be painful thanks to .NET Aspire Maddy Montaquila (Leger) 10:00 am Introducing Artificial Intelligence to your application Jordan Matthiesen 11:00 am Modernizing your desktop: From WinForms to Blazor, Azure, and AI Santiago Arango Toro Save the Date! .NET Conf: Focus on Modernization is a free, two-day livestream event that you won’t want to miss. Tune in on April 22 and 23, 2025, ask questions live, and learn how to get your .NET applications ready for the AI revolution. Save the date! Stay tuned for more updates and detailed session information. We can’t wait to see you there!Code the Future with Java and AI – Join Me at JDConf 2025
JDConf 2025 is just around the corner, and whether you’re a Java developer, architect, team leader, or decision maker I hope you’ll join me as we explore how Java is evolving with the power of AI and how you can start building the next generation of intelligent applications today. Why JDConf 2025? With over 22 expert-led sessions and 10+ hours of live content, JDConf is packed with learning, hands-on demos, and real-world solutions. You’ll hear from Java leaders and engineers on everything from modern application design to bringing AI into your Java stack. It’s free, virtual and your chance to connect from wherever you are. (On-demand sessions will also be available globally from April 9–10, so you can tune in anytime from anywhere.) Bring AI into Java Apps At JDConf 2025, we are going beyond buzzwords. We’ll show you how to bring AI into real Java apps, using patterns and tools that work today. First, we’ll cover Retrieval-Augmented Generation (RAG), a design pattern where your app retrieves the right business data in real time, and combines it with AI models to generate smart, context-aware responses. Whether it is answering support queries, optimizing schedules, or generating insights, RAG enables your app to think in real time. Second, we’ll introduce AI agents -- software entities that do more than respond. They act. Think about automating production line scheduling at an auto manufacturer or rebooking delayed flights for passengers. These agents interact with APIs, reason over data, and make decisions, all without human intervention. Third, we’ll explore the complete AI application platform on Azure. It is built to work with the tools Java developers already know - from Spring Boot to Quarkus - and includes OpenAI and many other models, vector search with PostgreSQL, and libraries like Spring AI and LangChain4j. Here are just two example stacks: Spring Boot AI Stack: any app hosting services like Azure Container Apps or App Service + Spring AI + OpenAI + PostgreSQL for business data and vector data store. Quarkus AI Stack: any app hosting services like Azure Container Apps or App Service + LangChain4j + OpenAI + PostgreSQL for business data and vector data store. This is how you turn existing Java apps into intelligent, interactive systems, without reinventing everything. Whether you are an experienced developer or just starting out, JDConf offers valuable opportunities to explore the latest advancements in Java, cloud, and AI technologies; gain practical insights; and connect with Java experts from across the globe – including Java 25, Virtual Threads, Spring Boot, Jakarta EE 12, AI developer experiences, Spring AI, LangChain4j, combining data and AI, automated refactoring to Java app code modernization. We’ll also show you how GitHub Copilot helps you modernize faster. GitHub Copilot's new “upgrade assistant” can help refactor your project, suggest dependency upgrades, and guide you through framework transitions, freeing you up to focus on innovation. Get the Right Fit for Your Java App And what if your apps run on JBoss, WebLogic, or Tomcat? We will walk you through how to map those apps to the right Azure service: Monoliths (JAR, WAR, EAR) → Deploy to App Service Microservices or containers → Use Azure Container Apps or AKS WebLogic & WebSphere → Lift and shift to Azure Virtual Machines JBoss EAP containers → Run on Azure Red Hat OpenShift You’ll get clear guidance on where your apps fit and how to move forward, with no guesswork or dead ends. Let's Code the Future, Together I’ll be there, along with Josh Long from the Spring AI community and Lize Raes from the LangChain4j community, delivering a technical keynote packed with practical insights. If you haven’t started building intelligent Java apps, you can start with JDConf. If you’ve already started on the journey, tune in to learn how you can enrich your experiences with the latest in tech. So, mark your calendar. Spread the word. Bring your team. JDConf 2025 is your place to build what is next with Java and AI. 👉 Register now at jdconf.com. Check out the 20+ exclusive sessions brought to you by Java experts from across the globe in all major time zones.
What's New in Azure App Service at Ignite 2024
Learn about the GA of sidecar extensibility on Linux and see team members demonstrating the latest tools for AI assisted web application migration and modernization as well as the latest updates to Java JBoss EAP on Azure App Service. Team members will also demonstrate integrating the Phi-3 small language model with a web application via the new sidecar extensibility using existing App Service hardware! Also new for this year’s Ignite, many topics that attendees see in App Service related sessions are also available for hands-on learning across multiple hands-on labs (HoLs). Don’t just watch team members demonstrating concepts on-stage, drop by one of the many HoL sessions and test drive the functionality yourself! Azure App Service team members will also be in attendance at the Expert Meetup area on the third floor in the Hub – drop by and chat if you are attending in-person! Additional demos, presentations and hands-on labs covering App Service are listed at the end of this blog post for easy reference. Sidecar Extensibility GA for Azure App Service on Linux Sidecar extensibility for Azure App Service on Linux is now GA! Linux applications deployed from source-code as well as applications deployed using custom containers can take advantage of sidecar extensibility. Sidecars enable developers to attach additional capabilities like third-party application monitoring providers, in-memory caches, or even local SLM (small language model) support to their applications without having to bake that functionality directly into their applications. Developers can configure up to four sidecar containers per application, with each sidecar being associated with its own container registry and (optional) startup command. Examples of configuring an OpenTelemetry collector sidecar are available in the documentation for both container-based applications and source-code based applications. There are also several recent blog posts demonstrating additional sidecar scenarios. One example walks through using a Redis cache sidecar as an in-memory cache to accelerate data retrieval in a web application (sample code here). Another example demonstrates adding a sidecar containing the Phi-3 SLM to a custom container web application (sample code here). Once the web app is running with the SLM sidecar, Phi-3 processes text prompts directly on the web server without the need to call remote LLMs or host models on scarce GPU hardware. Similar examples for source deployed applications are available in the Ignite 2024 hands on lab demonstrating sidecars. Exercise three walks through attaching an OTel sidecar to a source-code based application, and exercise four shows how to attach a Phi-3 sidecar to a source-code based application. Looking ahead to the future, App Service will be adding “curated sidecars” to the platform to make it easier for developers to integrate common sidecar scenarios. Development is already underway to include options for popular third-party application monitoring providers, Redis cache support, as well as a curated sidecar encapsulating the Phi-3 SLM example mentioned earlier. Stay tuned for these enhancements in the future! If you are attending Microsoft Ignite 2024 in person, drop by the theater session “Modernize your apps with AI without completely rewriting your code” (session code: THR 614) which demonstrates using sidecar extensibility to add Open Telemetry monitoring as well as Phi-3 SLM support to applications on App Service for Linux! .NET 9 GA, JBoss EAP and More Language Updates! With the recent GA of .NET 9 last week developers can deploy applications running .NET 9 GA on both Windows and Linux variants of App Service! Visual Studio, Visual Studio Code, Azure DevOps and GitHub Actions all support building and deploying .NET 9 applications onto App Service. Start a new project using .NET 9 or upgrade your existing .NET applications in-place and take advantage of .NET 9! For JBoss EAP on App Service for Linux, customers will soon be able to bring their existing JBoss licenses with them when moving JBoss EAP workloads onto App Service for Linux. This change will make it easier and more cost effective than ever for JBoss EAP customers to migrate existing workloads to App Service, including JBoss versions 7.3, 7.4 and 8.0! As a quick reminder, last month App Service also announced reduced pricing for JBoss EAP licenses (for net-new workloads) as well as expanded hardware support (both memory-optimized and Free tier are now supported for JBoss EAP applications). App Service is planning to release both Node 22 and Python 3.13 onto App Service for Linux with expected availability in December! Python 3.13 is the latest stable Python release which means developers will be able to leverage this version with confidence given long term support runs into 2029. Node 22 is the latest active LTS release of Node and is a great version for developers to adopt with its long-term support lasting into 2026. A special note for Linux Python developers, App Service now supports “auto-instrumentation” in public preview for Python versions 3.8 through 3.12. This makes it trivial for source-code based Python applications to enable Application Insights monitoring for their applications by simply turning the feature “on” in the Azure Portal. If you ever thought to yourself that it can be a hassle setting up application monitoring and hence find yourself procrastinating, this is the monitoring feature for you! Looking ahead just a few short weeks until December, App Service also plans to release PHP 8.4 for developers on App Service for Linux. This will enable PHP developers to leverage the latest fully supported PHP release with an expected support cycle stretching into 2028. For WordPress customers Azure App Service has added support for managed identities when connecting to MySQL database as well as storage accounts. The platform has also transitioned WordPress from Alpine Linux to Debian, aligning with App Service for Linux to offer a more secure platform. Looking ahead, App Service is excited to introduce some new features by the end of the year, including an App Service plugin for WordPress! This plugin will enable users to manage WordPress integration with Azure Communication Services email, set up Single Sign-On using Microsoft Entra ID, and diagnose performance bottlenecks. Stay tuned for upcoming WordPress announcements! End-to-End TLS & Min TLS Cipher Suite are now GA End-to-end TLS encryption for public multi-tenant App Service is now GA! When E2E TLS is configured, traffic between the App Service frontends and individual workers is secured using a platform supplied TLS certificate. This additional level of security is available for both Windows and Linux sites using Standard SKU and above as well as Isolatedv2 SKUs. You can enable this feature easily in the Azure Portal by going to your resource, clicking the “Configuration” blade and turning the feature “On” as shown below: Configuration of the minimum TLS cipher suite for a web application is also GA! With this feature developers can choose from a pre-determined list of cipher suites. When a minimum cipher suite is selected, the App Service frontends will reject any incoming requests that use a cipher suite weaker than the selected minimum cipher suite. This feature is supported for both Windows and Linux applications using Basic SKU and higher as well as Isolatedv2 SKUs. You configure a minimum TLS cipher suite in the Azure Portal by going to the “Configuration” blade for a website and selecting “Change” for the Minimum Inbound TLS Cipher Suite setting. In the resulting blade (shown below) you can select the minimum cipher suite for your application: To learn more about these and other TLS features on App Service, please refer to the App Service TLS overview. AI-Powered Conversational Diagnostics Building on the Conversational Diagnostics AI-powered tool and the guided decision making path introduced in Diagnostic Workflows, the team has created a new AI-driven natural language-based diagnostics solution for App Service on Linux. The new solution brings together previous functionality to create an experience that comprehends user intent, selects the appropriate Diagnostic Workflow, and keeps users engaged by providing real-time updates and actionable insights through chat. Conversational Diagnostics also provides the grounding data that the generative AI back-end uses to produce recommendations thus empowering users to check the conclusions. The integration of Conversational Diagnostics and Diagnostic Workflows marks a significant advancement in the platform’s diagnostic capabilities. Stay tuned for more updates and experience the transformative power of Generative AI-driven diagnostics firsthand! App Service Migration and Modernization The team just recently introduced new architectural guidance around evolving and modernizing web applications with the Modern Web Application pattern for .NET and Java! This guidance builds on the Reliable Web App pattern for .NET and Java as well as the Azure Migrate application and code assessment tool. With the newly released Modern Web Application guidance, there is a well-documented path for migrating web applications from on-premises/VM deployments using the application and code assessment tool, iterating and evolving web applications with best practices using guidance from the Reliable Web App pattern, and subsequently going deeper on modernization and re-factoring following guidance from the Modern Web Application pattern. Best of all customers can choose to “enter” this journey at any point and progress as far down the modernization path as needed based on their unique business and technical requirements! As a quick recap on the code assessment tool, it is a guided experience inside of Visual Studio with GitHub Copilot providing actionable guidance and feedback on recommended changes needed to migrate applications to a variety of Azure services including Azure App Service. Combined with AI-powered Conversational Diagnostics (mentioned earlier), developers now have AI-guided journeys supporting them from migration all the way through deployment and runtime operation on App Service! Networking and ASE Updates As of November 1, 2024, we are excited to announce that App Service multi-plan subnet join is generally available across all public Azure regions! Multi-plan subnet join eases network management by reducing subnet sprawl, enabling developers to connect multiple app service plans to a single subnet. There is no limit to the number of app service plans that connect to a single subnet. However, developers should keep in mind the number of available IPs since tasks such as changing the SKU for an app service plan will temporarily double the number of IP addresses used in a connected subnet. For more information as well as examples on using multi-plan subnet join see the documentation! App Service also recently announced GA of memory optimized options for Isolatedv2 on App Service Environment v3. The new memory-optimized options range from two virtual cores with 16 GB RAM in I1mv2 (compared to two virtual cores, 8 GB RAM in I1v2) all the way up to 32 virtual cores with 256 GB RAM in I5mv2. The new plans are available in most regions. Check back regularly to see if your preferred region is supported. For more details on the technical specifications of these plans, as well as information on the complete range of tiers and plans for Microsoft Azure App Service, visit our pricing page. Using services such as Application Gateway and Azure Front Door with App Service as entry points for client traffic is a common scenario that many of our customers implement. However, when using these services together, there are integration challenges around the default cookie domain for HTTP cookies, including the ARRAffinity cookie used for session affinity. App Service collaborated with the Application Gateway team to introduce a simple solution that addresses the session affinity problem. App Service introduced a new session affinity proxy configuration setting in October which tells App Service to always set the hostname for outbound cookies based on the upstream hostname seen by Application Gateway or Azure Front Door. This simplifies integration with a single-click experience for App Service developers who front-end their websites using one of Azure’s reverse proxies, and it solves the challenge of round-tripping the ArrAffinity cookie when upstream proxies are involved. Looking ahead to early 2025, App Service will shortly be expanding support for IPv6 to include both inbound and outbound connections (currently only inbound connections are supported). The current public preview includes dual-stack support for both IPv4 and IPv6, allowing for a smooth transition and compatibility with existing systems. Read more about the latest status of the IPv6 public preview on App Service here ! Lastly, the new application naming and hostname convention that was rolled out a few months earlier for App Service is now GA for App Service. The platform has also extended this new naming convention to Azure Functions where it is now available in public preview for newly created functions. To learn more about the new naming convention and the protection it provides against subdomain takeover take a look at the introductory blog post about the unique default hostname feature. Upcoming Availability Zone Improvements New Availability Zone features are currently rolling out that will make zone redundant App Service deployments more cost efficient and simpler to manage in early 2025! The platform will be changing the minimum requirement for enabling Availability Zones to two instances instead of three, while still maintaining a 99.99% SLA. Many existing app service plans with two or more instances will also automatically become capable of supporting Availability Zones without requiring additional setup. Additionally, the zone redundant setting will be mutable throughout the life of an app service plan. This upcoming improvement will allow customers on Premium V2, Premium V3, or Isolated V2 plans, to toggle zone redundancy on or off as needed. Customers will also gain enhanced visibility into Availability Zone information, including physical zone placement and counts. As a sneak peek into the future, the screenshot below shows what the new experience will look like in the Azure Portal: Stay tuned for Availability Zone updates coming to App Service in early 2025! Next Steps Developers can learn more about Azure App Service at Getting Started with Azure App Service. Stay up to date on new features and innovations on Azure App Service via Azure Updates as well as the Azure App Service (@AzAppService) X feed. There is always a steady stream of great deep-dive technical articles about App Service as well as the breadth of developer focused Azure services over on the Apps on Azure blog. Azure App Service (virtually!) attended the recently completed November .Net Conf 2024. App Service functionality was featured showing a .NET 9.0 app using Azure Sql’s recently released native vector data type support that enables developers to perform hybrid text searches on Azure Sql data using vectors generated via Azure OpenAI embeddings! And lastly take a look at Azure App Service Community Standups hosted on the Microsoft Azure Developers YouTube channel. The Azure App Service Community Standup series regularly features walkthroughs of new and upcoming features from folks that work directly on the product! Ignite 2024 Session Reference (Note: some sessions/labs have more than one timeslot spanning multiple days). (Note: all times below are listed in Chicago time - Central Standard Time). Modernize your apps with AI without completely rewriting your code Modernize your apps with AI without completely rewriting your code [Note: this session includes a demonstration of the Phi-3 sidecar scenario] Wednesday, November 20 th 1:00 PM - 1:30 PM Central Standard Time Theater Session – In-Person Only (THR614) McCormick Place West Building – Level 3, Hub, Theater C Unlock AI: Assess your app and data estate for AI-powered innovation Unlock AI: Assess your app and data estate for AI-powered innovation Wednesday, November 20 th 1:15 PM – 2:00 PM Central Time McCormick Place West Building – Level 1, Room W183c Breakout and Recorded Session (BRK137) Modernize and scale enterprise Java applications on Azure Modernize and scale enterprise Java applications on Azure Thursday, November 21 st 8:30 AM - 9:15 AM Central Time McCormick Place West Building – Level 1, Room W183c Breakout and Recorded Session (BRK147) Assess apps with Azure Migrate and replatform to Azure App Service Assess apps with Azure Migrate and replatform to Azure App Service Tuesday, November 19 th 1:15 PM - 2:30 PM Central Time McCormick Place West Building – Level 4, Room W475 Hands on Lab – In-Person Only (LAB408) Integrate GenAI capabilities into your .NET apps with minimal code changes Integrate GenAI capabilities into your .NET apps with minimal code changes [Note: Lab participants will be able to try out the Phi-3 sidecar scenario in this lab.] Wednesday, November 20 th 8:30 AM - 9:45 AM Central Time McCormick Place West Building – Level 4, Room W475 Hands on Lab – In-Person Only (LAB411) Assess apps with Azure Migrate and replatform to Azure App Service Assess apps with Azure Migrate and replatform to Azure App Service Wednesday, November 20 th 6:30 PM - 7:45 PM Central Time McCormick Place West Building – Level 4, Room W470b Hands on Lab – In-Person Only (LAB408-R1) Integrate GenAI capabilities into your .NET apps with minimal code changes Integrate GenAI capabilities into your .NET apps with minimal code changes [Note: Lab participants will be able to try out the Phi-3 sidecar scenario in this lab.] Thursday, November 21 st 10:15 AM - 11:30 AM Central Time McCormick Place West Building – Level 1, Room W180 Hands on Lab – In-Person Only (LAB411-R1) Assess apps with Azure Migrate and replatform to Azure App Service Assess apps with Azure Migrate and replatform to Azure App Service Friday, November 22 nd 9:00 AM – 10:15 AM Central Time McCormick Place West Building – Level 4, Room W474 Hands on Lab – In-Person Only (LAB408-R2)Reference Architecture for a High Scale Moodle Environment on Azure
Introduction Moodle is an open-source learning platform that was developed in 1999 by Martin Dougiamas, a computer scientist and educator from Australia. Moodle stands for Modular Object-Oriented Dynamic Learning Environment, and it is written in PHP, a popular web programming language. Moodle aims to provide educators and learners with a flexible and customizable online environment for teaching and learning, where they can create and access courses, activities, resources, and assessments. Moodle also supports collaboration, communication, and feedback among users, as well as various plugins and integrations with other systems and tools. Moodle is widely used around the world by schools, universities, businesses, and other organizations, with over 100 million registered users and 250,000 registered sites as of 2020. Moodle is also supported by a large and active community of developers, educators, and users, who contribute to its development, documentation, translation, and support. [URL] is the official website of the Moodle project, where anyone can download the software, join the forums, access the documentation, participate in events, and find out more about Moodle. Goal The goal for this architecture is to have a Moodle environment that can handle 400k concurrent users and scale in and out its application resources according to usage. Using Azure managed services to minimize operational burden was a design premise because standard Moodle reference architectures are based on Virtual Machines that comes with a heavy operational cost. Challenges Being a monolith application, scaling Moodle in a modern cloud native environment is challenging. We choose to use Kubernetes as its computing provider due to the fact that it allow us to build a Moodle artifact in an immutable way that allows it to scale out and in when needed in a fast and automatic way and also recover from potential failures by simply recreating its Deployments without the need to maintain Virtual Machine resources, introducing the concept of pets vs cattle[1] to a scenario that at first glance wouldn't be feasible. Since Moodle is written in PHP it has no concept of database polling, creating a scenario where its underlying database is heavily impacted by new client requests, making it necessary to use an external database pooling solution that had to be custom tailored in order to handle the amount of connections for a heavy-traffic setup like this instead of using Azure Database for PostgreSQL's built-in pgbouncer. The same effect is also observed in its Redis implementation, where a custom Redis cluster had to be created, whereas using Azure Cache for Redis would incur prohibitive costs due to the way it is set up for a more general usage. 1 - https://learn.microsoft.com/en-us/dotnet/architecture/cloud-native/definition#the-cloud Architecture This architecture uses Azure managed (PaaS) components to minimize operational burden by using Azure Kubernetes Service to run Moodle, Azure Storage Account to host course content, Azure Database for PostgreSQL Flexible Server as its database and Azure Front Door to expose the application to the public as well as caching commonly used assets. The solution also leverages Azure Availability Zones to distribute its component across different zones in the region to optimize its availability. Provisioning the solution The provisioning has two parts: setting up the infrastructure and the application. The first part uses Terraform to deploy easily. The second part involves creating Moodle's database and configuring the application for optimal performance based on the templates, number of users, etc. and installing templates, courses, plugins etc. The following steps walk you through all tasks needed to have this job done. Clone the repository $ git clone https://github.com/Azure-Samples/moodle-high-scale Provision the infrastructure $ cd infra/ $ az login $ az group create --name moodle-high-scale --location <region> $ terraform init $ terraform plan -var moodle-environment=production $ terraform apply -var moodle-environment=production $ az aks get-credentials --name moodle-high-scale --resource-group moodle-high-scale Provision the Redis Cluster $ cd ../manifests/redis-cluster $ kubectl apply -f redis-configmap.yaml $ kubectl apply -f redis-cluster.yaml $ kubectl apply -f redis-service.yaml Wait for all the replicas to be running $ ./init.sh Type 'yes' when prompted. Deploy Moodle and its services Change image in moodle-service.yaml and also adjust the moodle data storage account name in the nfs-pv.yaml (see commented lines in the files) $ cd ../../images/moodle $ az acr build --registry moodlehighscale<suffix> -t moodle:v0.1 --file Dockerfile . $ cd ../../manifests $ kubectl apply -f pgbouncer-deployment.yaml $ kubectl apply -f nfs-pv.yaml $ kubectl apply -f nfs-pvc.yaml $ kubectl apply -f moodle-service.yaml $ kubectl -n moodle get svc –watch Provision the frontend configuration that will be used to expose Moodle and its assets publicly $ cd ../frontend $ terraform init $ terraform plan $ terraform apply Approve the private endpoint connection request from Frontdoor in moodle-svc-pls resource. Private Link Services > moodle-svc-pls > Private Endpoint Connections > Select the request from Front Door and click on Approve. Install database $ kubectl -n moodle exec -it deployment/moodle-deployment -- /bin/bash $ php /var/www/html/admin/cli/install_database.php --adminuser=admin_user --adminpass=admin_pass --agree-license Deploy Moodle Cron Change image in moodle-cron.yaml $ cd ../manifests $ kubectl apply -f moodle-cron.yaml Your Moodle installation is now ready to use! Conclusion You can create a Moodle environment that is scalable and reliable in minutes with a very simple approach, without having to deal with the hassle of operating its parts that normally comes with standard Moodle installations.Introducing Serverless GPUs on Azure Container Apps
We're excited to announce the public preview of Azure Container Apps Serverless GPUs accelerated by NVIDIA. This feature provides customers with NVIDIA A100 GPUs and NVIDIA T4 GPUs in a serverless environment, enabling effortless scaling and flexibility for real-time custom model inferencing and other machine learning tasks. Serverless GPUs accelerate the speed of your AI development team by allowing you to focus on your core AI code and less on managing infrastructure when using NVIDIA accelerated computing. They provide an excellent middle layer option between Azure AI Model Catalog's serverless APIs and hosting models on managed compute. It provides full data governance as your data never leaves the boundaries of your container while still providing a managed, serverless platform from which to build your applications. Serverless GPUs are designed to meet the growing demands of modern applications by providing powerful NVIDIA accelerated computing resources without the need for dedicated infrastructure management. "Azure Container Apps' serverless GPU offering is a leap forward for AI workloads. Serverless NVIDIA GPUs are well suited for a wide array of AI workloads from real-time inferencing scenarios with custom models to fine-tuning. NVIDIA is also working with Microsoft to bring NVIDIA NIM microservices to Azure Container Apps to optimize AI inference performance.” - Dave Salvator, Director, Accelerated Computing Products, NVIDIA Key benefits of serverless GPUs Scale-to zero GPUs: Support for serverless scaling of NVIDIA A100 and T4 GPUs. Per-second billing: Pay only for the GPU compute you use. Built-in data governance: Your data never leaves the container boundary. Flexible compute options: Choose between NVIDIA A100 and T4 GPUs. Middle-layer for AI development: Bring your own model on a managed, serverless compute platform. Scenarios Whether you choose to use NVIDIA A100 or T4 GPUs will depend on the types of apps you're creating. The following are a couple example scenarios. For each scenario with serverless GPUs, you pay only for the compute you use with per-second billing, and your apps will automatically scale in and out from zero to meet the demand. NVIDIA T4 Real-time and batch inferencing: Using custom open-source models with fast startup times, automatic scaling, and a per-second billing model, serverless GPUs are ideal for dynamic applications that don't already have a serverless API in the model catalog. NVIDIA A100 Compute intensive machine learning scenarios: Significantly speed up applications that implement fine-tuned custom generative AI models, deep learning, or neural networks. High performance computing (HPC) and data analytics: Applications that require complex calculations or simulations, such as scientific computing and financial modeling as well as accelerated data processing and analysis among massive datasets. Get started with serverless GPUs Serverless GPUs are now available for workload profile environments in West US 3, Australia East, and Sweden Central regions with more regions to come. You will need to have quota enabled on your subscription in order to use serverless GPUs. By default, all Microsoft Enterprise Agreement customers will have one quota. If additional quota is needed, please request it here. Note: In order to achieve the best performance with serverless GPUs, use an Azure Container Registry (ACR) with artifact streaming enabled for your image tag. Follow steps here to enable artifact streaming on your ACR. From the portal, you can select to enable GPUs for your Consumption app in the container tab when creating your Container App or your Container App Job. You can also add a new consumption GPU workload profile to your existing Container App environment through the workload profiles UX in portal or through the CLI commands for managing workload profiles. Deploy a sample Stable Diffusion app To try out serverless GPUs, you can use the stable diffusion image which is provided as a quickstart during the container app create experience: In the container tab select the Use quickstart image box. In the quickstart image dropdown, select GPU hello world container. If you wish to pull the GPU container image into your own ACR to enable artifact streaming for improved performance, or if you wish to manually enter the image, you can find the image at mcr.microsoft.com/k8se/gpu-quickstart:latest. For full steps on using your own image with serverless GPUs, see the tutorial on using serverless GPUs in Azure Container Apps. Learn more about serverless GPUs With serverless GPUs, Azure Container Apps now simplifies the development of your AI applications by providing scale-to-zero compute, pay-as you go pricing, reduced infrastructure management, and more. To learn more, visit: Using serverless GPUs in Azure Container Apps (preview) | Microsoft Learn Tutorial: Generate images using serverless GPUs in Azure Container Apps (preview) | Microsoft LearnDeploy Smarter, Scale Faster – Secure, AI-Ready, Cost-Effective Kubernetes Apps at Your Fingertips!
In our previous blog post, we explored the exciting launch of Kubernetes Apps on Azure Marketplace. This follow-up blog will take you a step further by demonstrating how to programmatically deploy Kubernetes Apps using tools like Terraform, Azure CLI, and ARM templates. As organizations scale their Kubernetes environments, the demand for secure, intelligent, and cost-effective deployments has never been higher. By programmatically deploying Kubernetes Apps through Azure Marketplace, organizations can harness powerful security frameworks, cost-efficient deployment options, and AI solutions to elevate their Azure Kubernetes Service (AKS) and Azure Arc-enabled clusters. This automated approach significantly reduces operational overhead, accelerates time-to-market, and allows teams to dedicate more time to innovation. Whether you're aiming to strengthen security, streamline application lifecycle management, or optimize AI and machine learning workloads, Kubernetes Apps on Azure Marketplace provide a robust, flexible, and scalable solution designed to meet modern business needs. Let’s explore how you can leverage these tools to unlock the full potential of your Kubernetes deployments. Secure Deployment You Can Trust Certified and Secure from the Start – Every Kubernetes app on Azure Marketplace undergoes a rigorous certification process and vulnerability scans before becoming available. Solution providers must resolve any detected security issues, ensuring the app is safe from the outset. Continuous Threat Monitoring – After publication, apps are regularly scanned for vulnerabilities. This ongoing monitoring helps to maintain the integrity of your deployments by identifying and addressing potential threats over time. Enhanced Security with RBAC – Eliminates the need for direct cluster access, reducing attack surfaces by managing permissions and deployments through Azure Role-Based Access Control (RBAC). Lowering Cost of your Applications If your organization has Azure Consumption Commitment (MACC) agreements with Microsoft, you can unlock significant cost savings when deploying your applications. Kubernetes Apps available on the Azure Marketplace are MACC eligible and you can gain the following benefits: Significant Cost Savings and Predictable Expenses – Reduce overall cloud costs with discounts and credits for committed usage, while ensuring stable, predictable expenses to enhance financial planning. Flexible and Comprehensive Commitment Usage – Allocate your commitment across various Marketplace solutions that maximizes flexibility and value for evolving business needs. Simplified Procurement and Budgeting – Benefit from unified billing and streamlined procurement to driving efficiency and performance. AI-Optimized Apps High-Performance Compute and Scalability - Deploy AI-ready apps on Kubernetes clusters with dynamic scaling and GPU acceleration. Optimize performance and resource utilization for intensive AI/ML workloads. Accelerated Time-to-Value - Pre-configured solutions reduce setup time, accelerating progress from proof-of-concept to production, while one-click deployments and automated updates keep AI environments up-to-date effortlessly. Hybrid and Multi-Cloud Flexibility - Deploy AI workloads seamlessly on AKS or Azure Arc-enabled Kubernetes clusters, ensuring consistent performance across on-premises, multi-cloud, or edge environments, while maintaining portability and robust security. Lifecycle Management of Kubernetes Apps Automated Updates and Patching – The auto-upgrade feature keeps your Kubernetes applications up-to-date with the latest features and security patches, seamlessly applied during scheduled maintenance windows to ensure uninterrupted operations. Our system guarantees automated consistency and reliability by continuously reconciling the cluster state with the desired declarative configuration and maintaining stability by automatically rolling back unauthorized changes. CI/CD Automation with ARM Integration – Leverage ARM-based APIs and templates to automate deployment and configuration, simplifying application management and boosting operational efficiency. This approach enables seamless integration with Azure policies, monitoring, and governance tools, ensuring streamlined and consistent operations. Flexible Billing Options for Kubernetes Apps We support a variety of billing models to suit your needs: Private Offers for Upfront Billing - Lock in pricing with upfront payments to gain better control and predictability over your expenditures. Multiple Billing Models - Choose from flexible billing options to suit your needs, including usage-based billing, where you pay per core, per node, or other usage metrics, allowing you to scale as required. Opt for flat-rate pricing for predictable monthly or annual costs, ensuring financial stability and peace of mind. Programmatic Deployments of Apps There are several ways of deploying Kubernetes app as follows: - Programmatically deploy using Terraform: Utilize the power of Terraform to automate and manage your Kubernetes applications. - Deploy programmatically with Azure CLI: Leverage the Azure CLI for straightforward, command-line based deployments. - Use ARM templates for programmatic deployment: Define and deploy your Kubernetes applications efficiently with ARM templates. - Deploy via AKS in the Azure portal: Take advantage of the user-friendly Azure portal for a seamless deployment experience. We hope this guide has been helpful and has simplified the process of deploying Kubernetes. Stay tuned for more tips and tricks, and happy deploying! Additional Links: Get started with Kubernetes Apps: https://aka.ms/deployK8sApp. Find other Kubernetes Apps listed on Azure Marketplace: https://aka.ms/KubernetesAppsInMarketplace For Customer support, please visit: https://learn.microsoft.com/en-us/azure/aks/aks-support-help#create-an-azure-support-request Partner with us: If you are an ISV or Azure partner interested in listing your Kubernetes App, please visit: http://aka.ms/K8sAppsGettingStarted Learn more about Partner Benefits: https://learn.microsoft.com/en-us/partner-center/marketplace/overview#why-sell-with-microsoft For Partner Support, please visit: https://partner.microsoft.com/support/?stage=1
