Copy Files to Azure VM using PowerShell Remoting
There are a couple of different cases you want to copy files to Azure virtual machines. To copy files to Azure VM, you can use PowerShell Remoting. This works with Windows and Linux virtual machines using Windows PowerShell 5.1 (Windows only) or PowerShell 6 (Windows and Linux). Check out my blog post at the ITOpsTalk.com about copying files from Windows to Linux using PowerShell Remoting. If you want to know more about how to copy Files to Azure VM using PowerShell Remoting, check out my post.WingetCreate: Keeping WinGet packages up-to-date!
In the ever-evolving landscape of software development, efficiency is key. Windows users have long awaited an experience, where the simplicity of installing, updating, and managing software could be as seamless as executing a single command. Enter Windows Package Manager, or WinGet, a powerful tool that reshapes the way we handle software packages on the Windows platform. WinGet brings the simplicity of Linux package managers to the Windows environment, enabling users to use the command-line for installing their favorite packages.Empowering the AI Generation: Microsoft's Open-Source Initiative
In a world increasingly driven by open collaboration and community-driven innovation, Microsoft has undergone a remarkable transformation. The tech giant is on a mission to provide students, startups, AI developers, and entrepreneurs with the tools and resources they need to build groundbreaking solutions. Embracing open source is at the heart of this journey.Exploring Azure OpenAI Assistants and Azure AI Agent Services: Benefits and Opportunities
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly turning to cloud-based solutions to harness the power of AI. Microsoft Azure offers two prominent services in this domain: Azure OpenAI Assistants and Azure AI Agent Services. While both services aim to enhance user experiences and streamline operations, they cater to different needs and use cases. This blog post will delve into the details of each service, their benefits, and the opportunities they present for businesses. Understanding Azure OpenAI Assistants What Are Azure OpenAI Assistants? Azure OpenAI Assistants are designed to leverage the capabilities of OpenAI's models, such as GPT-3 and its successors. These assistants are tailored for applications that require advanced natural language processing (NLP) and understanding, making them ideal for conversational agents, chatbots, and other interactive applications. Key Features Pre-trained Models: Azure OpenAI Assistants utilize pre-trained models from OpenAI, which means they come with a wealth of knowledge and language understanding out of the box. This reduces the time and effort required for training models from scratch. Customizability: While the models are pre-trained, developers can fine-tune them to meet specific business needs. This allows for the creation of personalized experiences that resonate with users. Integration with Azure Ecosystem: Azure OpenAI Assistants seamlessly integrate with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This enables businesses to build comprehensive solutions that leverage multiple Azure capabilities. Benefits of Azure OpenAI Assistants Enhanced User Experience: By utilizing advanced NLP capabilities, Azure OpenAI Assistants can provide more natural and engaging interactions. This leads to improved customer satisfaction and loyalty. Rapid Deployment: The availability of pre-trained models allows businesses to deploy AI solutions quickly. This is particularly beneficial for organizations looking to implement AI without extensive development time. Scalability: Azure's cloud infrastructure ensures that applications built with OpenAI Assistants can scale to meet growing user demands without compromising performance. Understanding Azure AI Agent Services What Are Azure AI Agent Services? Azure AI Agent Services provide a more flexible framework for building AI-driven applications. Unlike Azure OpenAI Assistants, which are limited to OpenAI models, Azure AI Agent Services allow developers to utilize a variety of AI models, including those from other providers or custom-built models. Key Features Model Agnosticism: Developers can choose from a wide range of AI models, enabling them to select the best fit for their specific use case. This flexibility encourages innovation and experimentation. Custom Agent Development: Azure AI Agent Services support the creation of custom agents that can perform a variety of tasks, from simple queries to complex decision-making processes. Integration with Other AI Services: Like OpenAI Assistants, Azure AI Agent Services can integrate with other Azure services, allowing for the creation of sophisticated AI solutions that leverage multiple technologies. Benefits of Azure AI Agent Services Diverse Use Cases: The ability to use any AI model opens a world of possibilities for businesses. Whether it's a specialized model for sentiment analysis or a custom-built model for a niche application, organizations can tailor their solutions to meet specific needs. Enhanced Automation: AI agents can automate repetitive tasks, freeing up human resources for more strategic activities. This leads to increased efficiency and productivity. Cost-Effectiveness: By allowing the use of various models, businesses can choose cost-effective solutions that align with their budget and performance requirements. Opportunities for Businesses Improved Customer Engagement Both Azure OpenAI Assistants and Azure AI Agent Services can significantly enhance customer engagement. By providing personalized and context-aware interactions, businesses can create a more satisfying user experience. For example, a retail company can use an AI assistant to provide tailored product recommendations based on customer preferences and past purchases. Data-Driven Decision Making AI agents can analyze vast amounts of data and provide actionable insights. This capability enables organizations to make informed decisions based on real-time data analysis. For instance, a financial institution can deploy an AI agent to monitor market trends and provide investment recommendations to clients. Streamlined Operations By automating routine tasks, businesses can streamline their operations and reduce operational costs. For example, a customer support team can use AI agents to handle common inquiries, allowing human agents to focus on more complex issues. Innovation and Experimentation The flexibility of Azure AI Agent Services encourages innovation. Developers can experiment with different models and approaches to find the most effective solutions for their specific challenges. This culture of experimentation can lead to breakthroughs in product development and service delivery. Enhanced Analytics and Insights Integrating AI agents with analytics tools can provide businesses with deeper insights into customer behavior and preferences. This data can inform marketing strategies, product development, and customer service improvements. For example, a company can analyze interactions with an AI assistant to identify common customer pain points, allowing them to address these issues proactively. Conclusion In summary, both Azure OpenAI Assistants and Azure AI Agent Services offer unique advantages that can significantly benefit businesses looking to leverage AI technology. Azure OpenAI Assistants provide a robust framework for building conversational agents using advanced OpenAI models, making them ideal for applications that require sophisticated natural language understanding and generation. Their ease of integration, rapid deployment, and enhanced user experience make them a compelling choice for businesses focused on customer engagement. Azure AI Agent Services, on the other hand, offer unparalleled flexibility by allowing developers to utilize a variety of AI models. This model-agnostic approach encourages innovation and experimentation, enabling businesses to tailor solutions to their specific needs. The ability to automate tasks and streamline operations can lead to significant cost savings and increased efficiency. Additional Resources To further explore Azure OpenAI Assistants and Azure AI Agent Services, consider the following resources: Agent Service on Microsoft Learn Docs Watch On-Demand Sessions Streamlining Customer Service with AI-Powered Agents: Building Intelligent Multi-Agent Systems with Azure AI Microsoft learn Develop AI agents on Azure - Training | Microsoft Learn Community and Announcements Tech Community Announcement: Introducing Azure AI Agent Service Bonus Blog Post: Announcing the Public Preview of Azure AI Agent Service AI Agents for Beginners 10 Lesson Course https://aka.ms/ai-agents-beginnersCelebrating One Year of Open at Microsoft
It's funny how time flies. ⌛Guess what? It's been a whole year of diving into the wonders of different open-source tools. It's been one year of working with product teams within Microsoft to showcase open-source tools, platforms and frameworks that merge innovation with collaboration. Surprising, right? Trust me, IT IS!Gitting Started! Using git in the Azure Cloud Shell!
Microsoft Azure Cloud Advocate Jay Gordon gives you the first steps in working with git on the Azure Cloud Shell. You'll see how to create your very first repo and branch on from the Cloud Shell using Bash. Docs - Overview of Azure Cloud Shell - https://aka.ms/overviewcs Azure Cloud Shell https://aka.ms/cloudshelljg
Multiple Node Pools on Azure Kubernetes Service
One of the most impressive parts of moving applications into the cloud is the ability to apply different types of computing power to your application based on use-case. By taking advantage of all the different compute types within Azure, users access to various CPUs and GPUs that can be implemented. No upfront costs or concerns about racking the gear. No fighting for better support with your vendor or having to buy the latest and greatest to keep up. Azure provides you with a number of options to help implement these various compute options. Different use cases will often require you to make decisions about what compute options you've selected. How will they improve portions of your application for the business? In this post we'll look at how you can use multiple types of compute options within a Kubernetes cluster in order to use different resources for different parts of their application. Getting Started This is a post on understanding Azure Kubernetes Service (AKS) node pools. This is a blog post that makes assumptions that you're done the Azure tutorials around fundamentals. It will also assume you know how to create and use the Azure Kubernetes Service to launch a cluster. If you'd like to start with these, check out these Microsoft Learn Modules and blog posts: Microsoft Docs Learn Azure Fundamentals Microsoft Docs Learn Introduction to Azure Kubernetes Service Kubernetes Terminology for Beginners Azure Kubernetes Service - A Beginner's Guide. What's a node in Kubernetes? From the official Kubernetes Docs The Kubernetes node has the services necessary to run application containers and be managed from the master systems. A node is a worker machine in Kubernetes, previously known as a minion. A node may be a VM or physical machine, depending on the cluster. Each node contains the services necessary to run pods and is managed by the master components. The services on a node include the container runtime, kubelet and kube-proxy. See The Kubernetes Node section in the architecture design doc for more details._ It could be your local computer using minikube, a server in a datacenter or a virtual machine in the cloud. What's a node pool? You can use different types of CPUs and storage with management for your AKS managed nodes. A subset of VM's like hardware and configuration. Scale them based on your utilization. From Create and manage multiple node pools for a cluster in AKS on Microsoft docs website: In Azure Kubernetes Service (AKS), nodes of the same configuration are grouped together into node pools. These node pools contain the underlying VMs that run your applications. The initial number of nodes and their size (SKU) is defined when you create an AKS cluster, which creates a default node pool. To support applications that have different compute or storage demands, you can create additional node pools. For example, use these additional node pools to provide GPUs for compute-intensive applications, or access to high-performance SSD storage. What's a GPU Optimized VM?: From Microsoft docs: GPU optimized VM sizes are specialized virtual machines available with single or multiple NVIDIA GPUs. These sizes are designed for compute-intensive, graphics-intensive, and visualization workloads. Solving a problem Even Fake companies need help Tailwind Traders is the world's biggest fake company with a reference app used for this and many other examples. You can review this video from Azure Friday about the reference apps. You can also review the application repository here, it's open to the world to build with all the tools in this blog post. The Tailwind Traders Website at its core is an e-commerce site with a number of extremely critical services that the company requires reliability and uptime in order to successfully execute new orders. In order for Tailwind Traders to understand their customers better, much of the experience the user has is stored in a NoSQL database in Azure Cosmos DB. Cosmos provides Tailwind Traders with a low latency and high throughput database that works with the MongoDB API. Daily, reports on this information are run and delivered into a web presentable front end. The reports app uses a number of algorithms that the data science team at Tailwind Traders developed along with the application engineer; these are extremely compute-intensive and seem to be taxing the capabilities of the more general-purpose CPU offerings in Azure. The Tailwind Traders engineering team recently looked at processing times of their daily reports for and recognized that even though they were wise enough to architect their application into microservices, they felt that the return time of some reports (nearly 8-9 hours) had a potential of being reduced by utilizing GPUs available in Microsoft Azure. Tailwind Traders will want to use standard CPUs for a portion of the web front end of my app. The company's CIO would like to have reports on their collected data processed quicker on GPUs in order for the business to best place the most popular and impressive products to potential customers based on data captured. The more information Tailwind Traders is able to gain on their customer's experience, the more they can improve it. Proposed Solution: TWT's team will build an AKS cluster with multiple node pools. The two pools will contain different types of computing power to handle the microserviced application workloads. To begin, the team will create a new cluster for AKS, in this case, nodepool01 that will handle the web app front end using a Bs2 series VM for the node pool. You can also create node pools and specify your specific CPU type with az cli tool rather than do so by the portal: # Create a resource group in East US az group create --name myResourceGroup --location eastus # Create a basic single-node AKS cluster az aks create \ --resource-group myResourceGroup \ --name cluster01 \ --vm-set-type VirtualMachineScaleSets \ --node-count 2 \ --generate-ssh-keys \ --kubernetes-version 1.15.7 \ --load-balancer-sku standard # Add a node pool to the cluster az aks nodepool add \ --resource-group myResourceGroup \ --cluster-name cluster01 \ --name nodepool02 \ --node-count 3 \ --kubernetes-version 1.15.7 This cluster will use B-series burstable VMs which are ideal for workloads that do not need the full performance of the CPU continuously, like web servers, small databases and development and test environments - exactly the solution for a web front end service and its required components such as a load balancer. The TWT team chose to create a secondary node pool nodepool02 that will handle my GPU intensive workloads, such as processing data for reports that are used to improve customer experience. The secondary node pool is created using a NC6s_v2 class virtual machine. The TWT tech team next can create a node pool using the az aks node pool add command again. This time, specify the name nodepool02 , and use the --node-vm-size parameter to specify the Standard_NC6 size: az aks nodepool add \ --resource-group myResourceGroup \ --cluster-name myAKSCluster \ --name nodepool02 \ --node-count 1 \ --node-vm-size Standard_NC6 \ --no-wait The TWT team can begin assigning specific pods to nodes by scheduling pods using taints and tolerations. By providing a dedicates pool of GPU backed nodes, TWT will now be able to run their reports using the power of single or multiple NVIDIA GPUs. More Info There's more documentation available to you to find out the best way to implement node pools with AKS. Check out the different links and videos provided before so you can begin using this powerful option for those who want to diversify compute options within their AKS cluster. Resources Microsoft Docs: az aks nodepool Create and manage multiple node pools for a cluster in Azure Kubernetes Service (AKS) GitHub Actions for deploying to Kubernetes service Azure Kubernetes Service (AKS) Multiple node pools in Azure Kubernetes Service (AKS) | Azure Friday Getting production ready in Kubernetes The Author If you're running into issues and need assistance with AKS or any other Azure service, reach out to me: Twitter: @jaydestro Twitch: jaydestro Github: jaydestro I am always happy to hear from developers and engineers who are trying to implement new services to improve their experience for their customers.
