The partnership between Microsoft and Anyscale introduces a managed Ray service on Azure, simplifying the transition from prototype to production for AI/ML workloads. It allows Python developers to efficiently run distributed workloads, leveraging Azure Kubernetes Service for scalability, while focusing on model performance and innovation without complex infrastructure management.</div><div class="read-more"><a href="" class="more-link">Continue reading</a>https://lnkd.in/geNrR_UW

𝐖𝐡𝐚𝐭 𝐈𝐬 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫 𝐢𝐧 𝐀𝐖𝐒 𝐋𝐚𝐦𝐛𝐝𝐚? If you’ve ever wondered how AWS Lambda manages to be so fast, secure and highly isolated, the answer is a powerful technology called Firecracker. 𝐖𝐡𝐚𝐭 𝐞𝐱𝐚𝐜𝐭𝐥𝐲 𝐢𝐬 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫? 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫 𝐢𝐬 𝐚𝐧 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 MicroVM technology built by AWS. It’s the engine that runs every Lambda function behind the scenes. Think of it as the middle ground between containers and VMs: • Faster than traditional VMs • More secure than containers • Ultra-lightweight (~5MB) Perfect for the scale Lambda operates at. 𝐇𝐨𝐰 𝐋𝐚𝐦𝐛𝐝𝐚 𝐮𝐬𝐞𝐬 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫 ? When you invoke a Lambda function: • AWS spins up a Firecracker MicroVM • Your runtime + code run inside this tiny VM • Firecracker provides strong isolation and millisecond-level startup times • Warm invocations reuse the same MicroVM 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐰𝐡𝐲 𝐋𝐚𝐦𝐛𝐝𝐚 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐲𝐨𝐮 𝐦𝐚𝐧𝐚𝐠𝐢𝐧𝐠 𝐚𝐧𝐲 𝐬𝐞𝐫𝐯𝐞𝐫𝐬. 𝐖𝐡𝐲 𝐢𝐬 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫 𝐬𝐨 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭? • Low cold start latency • Strong isolation for multi-tenant environments • Ideal for scaling from 0 to 10,000s of instances 𝐅𝐢𝐫𝐞𝐜𝐫𝐚𝐜𝐤𝐞𝐫 𝐢𝐬 𝐰𝐡𝐚𝐭 𝐦𝐚𝐤𝐞𝐬 “𝐬𝐞𝐫𝐯𝐞𝐫𝐥𝐞𝐬𝐬” 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞 𝐚𝐭 𝐀𝐖𝐒 𝐬𝐜𝐚𝐥𝐞. #AWS #Tech

Karpenter is taking over. According to Datadog’s latest State of Containers & Serverless report, Karpenter adoption is up ~22%, while Cluster Autoscaler is down ~17%. Why it matters: Karpenter provisions nodes just-in-time, matching compute to actual pod needs. It means faster scaling, lower idle costs, and simpler operations. The shift shows how teams are embracing efficiency and autonomy in Kubernetes platforms. https://lnkd.in/dmUzFk3F #Kubernetes #Karpenter #PlatformEngineering #DevOps #CloudEfficiency

🔥 Monoliths are fading fast, and serverless microservices are becoming the new foundation of backend engineering. I’ve been reading more about how serverless computing and microservices are merging into the new default architecture for modern backend systems. Instead of maintaining one large monolithic application, we’re building systems made of smaller, event-driven services, each independently deployable and automatically scalable. ☁️ Platforms like Azure Functions, AWS Lambda, and Google Cloud Run make this possible, letting developers focus on logic instead of infrastructure. 💡 It’s a big shift in mindset: • Designing for independence, not centralization • Thinking in events and workflows instead of endpoints • Prioritizing observability, retries, and idempotence from day one The combination of microservices and serverless is redefining backend simplicity and it is powerful, scalable, and cloud-native by design. #Serverless #Microservices #Azure #CloudComputing #BackendDevelopment #dotnet #SoftwareEngineering

Last night the AWS service faced an outage. The issue was traced to DNS resolution failure linked to DynamoDB api. What surprised me is how many AWS services use Dynamodb internally- so when it went down several services could not update their state, consequently leading to cascading failure. 1) AWS Lambda: Used by dynamodb streams for event triggers and to maintain state of function execution. 2) AWS CloudFormation – keeps stack operation states and deployment locks in DynamoDB. 3) AWS Auto Scaling – stores scaling policies and activity logs there. 4) API Gateway / AppSync – save API keys, rate limit info, and request mappings. 5) EventBridge / Kinesis – use it for event and stream coordination. 6) S3 – even uses DynamoDB indexes for replication and inventory metadata. I learned the the interconnectedness of distributed system and importance of reliability engineering #DevOps #SRE #AWS

☁️🏙️  Resilience isn’t a nice-to-have—it’s revenue protection and customer trust, especially when complex, distributed systems fail in surprising ways. With Vertical Relevance’s Experiment Generator, you get #automated, reusable resilience tests baked into your pipelines—fewer outages, faster recovery, audit-ready evidence, and the confidence to ship faster. https://lnkd.in/eHrXAzDZ Amazon Web Services (AWS) | AWS for Financial Services | AWS Partners | #AWS | #resilience #genAI | #APNProud | #AWSPartners | #SRE #DevOps | #ChaosEngineering | #Serverless | #CICD | #Reliability | #PlatformEngineering | #verticalrelevance

How to Choose the Right EC2 Instance Type — Simplified for DevOps Engineers Choosing the right EC2 instance can make or break your AWS deployment. Many teams overpay or underperform simply because they don’t match their workloads correctly. Here’s a quick decision flow 👇 ✅ Compute-Optimized (C family) → For CPU-heavy workloads ✅ Memory-Optimized (R family) → For databases, caching ✅ Storage-Optimized (I/D family) → For high-speed local storage ✅ GPU/Accelerated (P/G/Inf/F) → For ML, rendering, graphics ✅ Burstable (T family) → For dev/test or low-cost workloads 💡 Pro Tip: Always benchmark your workload and monitor CPU, RAM, and I/O patterns before scaling. 📊 Save this visual for your next AWS architecture design. --- 👨💻 By The DevOps Tooling Empowering DevOps, one tool at a time. 🌐https://lnkd.in/gMaxke92 #AWS #DevOps #CloudComputing #EC2 #AWSCommunity #InfrastructureAsCode #CloudEngineer #TheDevOpsTooling #Terraform #Kubernetes #FinOps #CloudArchitecture #AWSTips #CostOptimization

🏙️ 🚀☁️ Resilience isn’t optional—it’s essential. As distributed systems grow, hidden failure modes can trigger costly outages. Build #resilience into your culture and your #pipelines — not just your postmortems. Vertical Relevance’s Experiment Generator is a centralized, #automated way to design and generate #resiliency experiments that teams can reuse across applications. ☁️ Centralized methodology and reusable scenarios 🚀 Automated experiment generation via CLI, ideal for CI/CD ☁️ Serverless on AWS (API Gateway, Lambda, DynamoDB, S3, SSM) 🚀  Broader adoption, more frequent testing, and easier gamedays Amazon Web Services (AWS) | AWS for Financial Services | AWS Partners | #AWS | #resilience | #genAI | #APNProud | #AWSPartners | #SRE #DevOps | #ChaosEngineering | #Serverless | #CICD | #Reliability | #PlatformEngineering

🚀 Understanding AWS Lambda Triggers 🔑 AWS Lambda is a powerful serverless compute service that automatically runs your code in response to events. Here are the most common triggers that activate Lambda functions: 🔹 Amazon S3: Triggered by object creation, deletion, or modification — perfect for automating workflows around data storage. 🔹 Amazon DynamoDB: Reacts to data changes via streams, enabling real-time data processing. 🔹 API Gateway: Invokes functions through HTTP/HTTPS requests, making it ideal for building serverless APIs. 🔹 CloudWatch Events / EventBridge: Schedule tasks or respond to system events with flexible rules. 🔹 Amazon SNS: Send notifications that trigger Lambda functions seamlessly. 🔹 Amazon SQS: Process messages from queues, ensuring reliable and asynchronous workflows. 🔹 Cognito: Handle user sign-up, sign-in, and other user pool events. 💡 Tip: Combining these triggers with Lambda functions enables scalable, event-driven applications and eliminating the need for managing servers! #AWS #Serverless #Lambda #CloudComputing #DevOps #Automation #triggers #interviewquestion

Ever heard of “serverless” and thought, “Sounds cool, but how does that actually *work* behind the scenes?” Spoiler: It’s not magic—just a clever evolution in how we build and scale applications. Serverless computing has been one of the hottest buzzwords in cloud and software engineering for a few years now. But beyond the hype, understanding what it truly means can unlock massive productivity gains. Here’s the quick rundown: Traditionally, we manage servers, CPUs, memory, scaling, downtime—you name it! With serverless, all that infrastructure management is abstracted away. You write **functions** (tiny bits of business logic), upload them, and the cloud provider *magically* handles execution, scaling, and maintenance. You pay only for the time your function is actually running, down to the millisecond. But let’s get concrete. Serverless is **event-driven**. That means your code runs *in response* to events—HTTP requests, database updates, file uploads, or even scheduled timers. AWS Lambda, Azure Functions, or Google Cloud Functions listen for these triggers and execute your code instantly. Why should you care? 1. **Scalability without headaches:** No more manually configuring load balancers or worrying about peak traffic. Serverless scales up and down automatically. 2. **Cost efficiency:** Instead of paying for idle VM time, you pay precisely for what you use—a game changer for startups and side projects. 3. **Faster time-to-market:** Focus on your code, not infrastructure. Deploy new features rapidly. 4. **Improved reliability:** Cloud providers invest heavily in uptime and security, sparing dev teams from reinventing the wheel. But of course, it’s not all sunshine and rainbows. Things like cold starts, debugging complexity, and vendor lock-in require careful consideration. If you haven’t dived deep into serverless yet, consider starting with a simple AWS Lambda function or Azure Function triggered by an HTTP request. It’s an eye-opener for sure! Curious how serverless might reshape your next project? Or already in the trenches with it? Would love to hear your stories! #Serverless #CloudComputing #DevOps #ProgrammingTips #TechTrends #SoftwareEngineering #AWS #Scalability