Abdullateef Lawal’s Post

This article highlights the operational weaknesses with GenAI workloads when compared to traditional microservices - giving birth to "GenOps". There are many approaches to deploying AI agents, from code-first DIY approaches to no-code agent managed builder environments. These approaches create a more complex, heterogeneous deployment landscape than what we have today with microservices applications and I have a feeling things will become more standardized as time passes. Model compliance, approval controls and prompt evaluation will soon be integrated into every underlying infrastructure (as we've seen with Cloudflare and now Google making moves) Luckily this functionality was developed by the team at LeakSignal in early 2023 and is already operational in production environments. Data in-transit classification and protection is the key to solving these and many other data-related problems within complex environments. https://lnkd.in/e7UbAvMQ

Breaking the Myth: Serverless Still Requires Operations! Many see “serverless” as hands-off, imagining it’s entirely self-managing. But as anyone using AWS Lambda (or similar services) knows, that’s far from true! Behind the scenes, serverless still requires operational oversight to ensure efficiency, reliability, and scalability. Here are some key questions to consider with serverless functions: 🔹 Concurrency: What’s the right level for our app? Too low, and we risk throttling; too high, and we may need cost control strategies. 🔹 Cold Starts: How do we mitigate latency, especially for latency-sensitive applications? Serverless isn’t exempt from startup delays when idle. 🔹 Memory Allocation: What’s the optimal memory setting? Serverless doesn’t inherently self-tune; choosing the right memory impacts both cost and performance. 🔹 Architecture Updates: As architecture evolves, so should our configurations. Are memory, timeouts, and other settings still optimized? Serverless enables us to focus more on code, but it doesn’t remove the need for thoughtful operations. Let’s bust the myth – serverless still demands strategic management to thrive in production! #Serverless #CloudOperations #AWSLambda #DevOps

During the talk with Tracy Bannon, we went through my 𝙅𝙤𝙪𝙧𝙣𝙚𝙮 𝙏𝙤 𝘾𝙡𝙤𝙪𝙙 chart, which includes 4 stages of migrating to a Cloud Computing environment. In 𝐒𝐭𝐚𝐠𝐞 3 (𝐑𝐞𝐟𝐚𝐜𝐭𝐨𝐫 - 𝐚𝐤𝐚, 𝐌𝐨𝐝𝐞𝐫𝐧𝐢𝐬𝐞), for the first time, we are now having to deal with the fact that we have to move away from our old data centre monolithic applications and into the brave world of loosely-coupled, 12-factor app, Cloud Native designs, using a modular or Microservices approach (although Microservices seem to be getting a bit of a bashing at the moment - more on that soon!) But 𝐒𝐭𝐚𝐠𝐞 3 is not just a technology shift. It's also a major, major cultural shift as well, as IT teams try to get their heads around DevOps/DevSecOps, Agile, CI/CD pipelines, GitOps, etc. How will current organisational processes and current operating model change? What is the impact, the skills and cost implications? In fact, this stage has been such a "hard part" for many organisations it's inevitable they will "make mistakes", so "leadership buy-in" is essential, to keep going when the going and continue fighting against "muscle memory", as Trac puts it so perfectly. Have a watch of the >𝙠𝙡𝙞𝙥𝙨_ excerpt and click the link below for the full talk on the ℂ𝕝𝕠𝕦𝕕 𝕋𝕙𝕖𝕣𝕒𝕡𝕚𝕤𝕥 YouTube channel and don't forget to 🆂🆄🅱🆂🅲🆁🅸🅱🅴: https://lnkd.in/en-xJXZy #publiccloud #cloudmigration #cloudjourney #cloudtherapist #klips #containers #kubernetes

Developers and DevOps teams should find LocalOps super obvious and clear to use and deploy to cloud. So we doubled down on our Documentation and produced 100 articles across our Developer docs and Help center. Checkout https://buff.ly/3SaqEqH #Developers #Build #SaaS #PrivateSaaS #DevOps #AWS

Managing #AI workloads doesn’t have to be complicated. With dstack on CUDO Compute, you can skip the heavy #DevOps setups and focus on what matters—building and deploying amazing #AImodels. From streamlined dev environments to scalable endpoints, we’ve got you covered. Curious? Dive into the details and see how easy AI infrastructure can be with us. Read the rest to learn more 👇

🚀 Exciting advancements in DevOps with AI! 🌟 Discover how leveraging agents for Amazon Bedrock can revolutionize your DevOps practices by interactively generating infrastructure as code. This AWS blog explores the integration of AI to streamline infrastructure management, enhancing efficiency and reducing manual effort. #DevOps #AI #Automation #AWS #IaC

Does Going Serverless = Instant Cost Savings? Huge potential for scalability and efficiency, but it's important to understand how they work before jumping in. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 containers need fixed resources. This often leads to waste. Manual scaling adds more complexity and cost. 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀, however, allocates resources only when needed. It scales automatically with demand. This reduces waste and lowers operational overhead. Stateless applications are easier to manage and scale in serverless environments. Key Considerations: -- Adjusts resources based on demand but 𝗺𝗶𝘀𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀 can still lead to inefficiencies. --𝗖𝗼𝗹𝗱 𝘀𝘁𝗮𝗿𝘁 𝗹𝗮𝘁𝗲𝗻𝗰𝘆 means delays during the first request can impact performance. -- Requires 𝘀𝘁𝗿𝗼𝗻𝗴 𝗮𝗰𝗰𝗲𝘀𝘀 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 and 𝘀𝗲𝗰𝗿𝗲𝘁𝘀 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. How to Run Serverless Containers on Kubernetes: 𝟭. 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺. Start with a Kubernetes platform like GKE, EKS, or AKS that fits your cloud ecosystem. 𝟮. 𝗦𝗲𝘁 𝗨𝗽 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀 𝗘𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁. Secure it with network policies, RBAC, and secrets management. 𝟯. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗮 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸. Use tools like Knative, Kubeless, or OpenFaaS to automate scaling and resource management. 𝟰. 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿𝗶𝘇𝗲 𝗬𝗼𝘂𝗿 𝗔𝗽𝗽𝘀. Break your apps into microservices or stateless functions. This increases flexibility and scalability. 𝟱. 𝗗𝗲𝗽𝗹𝗼𝘆 𝘄𝗶𝘁𝗵 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀. Use deployment manifests for clear resource allocation, and apply version control for stability. 𝟲. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴. Leverage Kubernetes' Horizontal Pod Autoscaler (HPA) and integrate CI/CD pipelines to automate deployment and scaling. 𝟳. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝗮𝗻𝗱 𝗠𝗮𝗻𝗮𝗴𝗲. Use tools like Prometheus and Grafana to track performance, resource usage, and errors. When executed correctly, serverless on Kubernetes can cut cloud costs by 𝘂𝗽 𝘁𝗼 𝟲𝟬%. But it takes the right strategy. We can help you get there. 📤 DM me “Kubernetes”, and let’s makes serverless work for you. #Serverless #Kubernetes #CloudOptimization #DevOps #CloudNative

𝗛𝗼𝘄 𝗔𝗪𝗦 𝗟𝗮𝗺𝗯𝗱𝗮 𝗪𝗼𝗿𝗸𝘀 𝗨𝗻𝗱𝗲𝗿 𝘁𝗵𝗲 𝗛𝗼𝗼𝗱 AWS Lambda revolutionizes cloud computing by offering serverless execution by abstracting infrastructure complexities. But under the hood, its performance is powered by intelligent orchestration. Here's an inside look at the infrastructure and execution lifecycle of Lambda functions: 🔍 𝗨𝗻𝗱𝗲𝗿𝗹𝘆𝗶𝗻𝗴 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 1️⃣ 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 𝗠𝗶𝗰𝗿𝗼𝗩𝗠𝘀 (𝗙𝗶𝗿𝗲𝗰𝗿𝗮𝗰𝗸𝗲𝗿): Lambda leverages Firecracker, a lightweight virtual machine manager. Each function runs securely in an isolated microVM those are optimized for short-lived, single-purpose tasks with minimal overhead. 2️⃣ 𝗦𝗲𝗿𝘃𝗲𝗿 𝗣𝗼𝗼𝗹𝘀 Lambda functions execute on dynamically managed EC2 instance pools which ensures scalability and concurrency without user intervention. 3️⃣ 𝗦𝗲𝗰𝘂𝗿𝗲 𝗜𝘀𝗼𝗹𝗮𝘁𝗶𝗼𝗻 Functions run in sandboxed environments with strict isolation, limiting networking and file system access for enhanced security. ⚙️ Function Execution Lifecycle: 1️⃣ 𝗖𝗼𝗹𝗱 𝗦𝘁𝗮𝗿𝘁: When a function is invoked for the first time or after a period of inactivity: Container Allocation: A new microVM is initialized or an idle one is reused. Code Deployment: Function code is fetched from S3 and loaded into the microVM. Runtime Initialization: The runtime (e.g., Python, Node.js) and dependencies are loaded, and environment variables are set. 2️⃣ 𝗪𝗮𝗿𝗺 𝗦𝘁𝗮𝗿𝘁: Subsequent invocations reuse the microVM and skip the initialization which results in lower latency. 3️⃣ 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: Lambda listens for events (e.g., API Gateway, S3, DynamoDB). The handler processes the event payload and returns a response. And responses are sent back to the caller or routed to destinations like SQS or SNS. 4️⃣ 𝗖𝗼𝗻𝘁𝗮𝗶𝗻𝗲𝗿 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: MicroVMs stay warm for a configurable period (15 mins) to handle repeated requests and are deallocated after inactivity to optimize resource usage. AWS Lambda's intelligent orchestration ensures secure, scalable, and efficient function execution, making it a crown jewel of serverless architecture. #AWSLambda #Serverless #CloudComputing #Firecracker #AWS #Microservices #DevOps #CloudArchitecture #Scalability #ServerlessArchitecture #ColdStart #CloudSecurity #InfrastructureAsCode #ModernDevelopment #TechInsights #SoftwareEngineering

𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗣𝗿𝗼𝘅𝘆: 𝗡𝗴𝗶𝗻𝘅 𝘃𝘀 𝗧𝗿𝗮𝗲𝗳𝗶𝗸! Web applications are becoming increasingly complex, with multiple services and microservices communicating with each other. So, In this digital, modern and AI world proxy servers have become an essential component of any modern infrastructure. Here are some key takeaways to consider when choosing between Nginx and Traefik: • Nginx is a veteran proxy that offers high performance, reliability, and scalability, making it a popular choice for production environments. • Traefik is a modern alternative, specifically designed for containerized applications, with features like automatic configuration, dynamic routing, and ease of use. • Complexity matters: Nginx might be the better choice for simple web applications, while Traefik's auto-configuration and dynamic routing make it ideal for complex, containerized apps. Want to learn more about which proxy server is right for you? Click here to read the full article on Medium. 🌐 Read the full article on Medium: https://lnkd.in/d5E7rKYP  ----------- 🔔 Follow for more insightful content on Cloud, DevOps, AI/Data, SRE, and Cloud Native. Subscribe to my new Biweekly Newsletter: https://lnkd.in/drAYuSNX 𝗟𝗶𝗸𝗲, 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗥𝗲𝗽𝗼𝘀𝘁! 👍 #Technology #Cloudcomputing #Devops #Kubernetes #Automation #Artificialintelligence #SoftwareEngineering #Programming #Softwaredesign