Cloud Application Deployment

💡 Research Spotlight: 𝗠𝗦𝗰 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝘂𝗯𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 (𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬) ��� 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬: 𝐌𝐚𝐦𝐛𝐚-𝐁𝐚𝐬𝐞𝐝 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐥𝐞 𝐂𝐥𝐨𝐮𝐝 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 𝐓𝐨𝐰𝐚𝐫𝐝𝐬 𝐍𝐞𝐭 𝐙𝐞𝐫𝐨 𝐆𝐨𝐚𝐥𝐬 🅰️ Read our latest publication led by our 𝐄𝐄𝐂𝐒-𝐐𝐌𝐔𝐋 𝐌𝐒𝐜 𝐀𝐥𝐮𝐦𝐧𝐢 Thananont Chevaphatrakul, sheds light on the utilisation of 𝗔𝗜 (𝗠𝗮𝗺𝗯𝗮) for 𝗖𝗹𝗼𝘂𝗱 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 to enable 𝗖𝗮𝗿𝗯𝗼𝗻 𝗡𝗲𝘂𝘁𝗿𝗮𝗹 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 to contribute towards 𝗡𝗲𝘁 𝗭𝗲𝗿𝗼 𝗧𝗮𝗿𝗴𝗲𝘁𝘀. 📚This work is published by an Open Access Future Internet MDPI Journal. Last week, Thananont Chevaphatrakul has passed his MSc with 𝐃𝐢𝐬𝐭𝐢𝐧𝐜𝐭𝐢𝐨𝐧 at Queen Mary School of Electronic Engineering and Computer Science of Queen Mary University of London 𝑯𝒊𝒈𝒉𝒍𝒊𝒈𝒉𝒕𝒔: 1️⃣ 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: MambaNet0 uses 𝗔𝗜 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴 model (𝗠𝗮𝗺𝗯𝗮) to manage resources effectively in the Google cloud environment, with the goal of improving 𝗲𝗻𝗲𝗿𝗴𝘆 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 and 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 for carbon-neutral cloud services. 2️⃣ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: The MambaNet0 framework is implemented using an Google Cloud’s Vertex AI environment, and the experimental results demonstrate that the 𝗠𝗮𝗺𝗯𝗮𝗡𝗲𝘁𝟬 enhances forecasting accuracy allowing for more precise resource allocation. 3️⃣𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗜𝗺𝗽𝗮𝗰𝘁: The MambaNet0 framework enables more sustainable and cost-effective operations that further support 𝗻𝗲𝘁-𝘇𝗲𝗿𝗼 𝗲𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲𝘀. 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: Are you interested in extending this work for modern applications? Open source code is released on https://lnkd.in/ejD-fdXx 📒 𝙊𝙥𝙚𝙣 𝘼𝙘𝙘𝙚𝙨𝙨 𝑳𝒊𝒏𝒌 𝒕𝒐 𝒕𝒉𝒆 𝒂𝒓𝒕𝒊𝒄𝒍𝒆: https://lnkd.in/ehFes__w 📺 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼: A YouTube video explains this work, enabling future authors to easily understand its workings: https://lnkd.in/eP2NAex3 Many thanks to co-author: Han Wang #EECS #QMUL #Cloudcomputing #Machinelearning #Sustainablecomputing #AI #researchpaper #computing #edge #Cloud #applications #IoT #computerscience #Research #industry #academics #journals #journal #qmul #postdoc #Scientificresearch #conference #PhD #university #publications #Computing #academiclife #ArtificialIntelligence #academia #engineering #Academic #NetZero

𝐌𝐨𝐬𝐭 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐚𝐢𝐥 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐭𝐡𝐞𝐲 𝐜𝐚𝐧 𝐧𝐨𝐭 𝐫𝐞𝐦𝐞𝐦𝐛𝐞�� 𝐂𝐨𝐧𝐭𝐞𝐱𝐭.  Here is the 10-step Roadmap to build Agents that actually work. From my experience,  successful deployments follow this exact progression: 1. Scope the Cognitive Contract • Define task domain, decision authority, error tolerance • Specify I/O schemas and action boundaries • Establish non-functional requirements (latency, cost, compliance) 2. Data Ingestion & Governance Layer • Integrate SharePoint, Azure SQL, Blob Storage pipelines • Normalize, chunk, and version content artifacts • Enforce RBAC, PII redaction, policy tagging 3. Semantic Representation Pipeline • Generate embeddings via Azure OpenAI embedding models • Vectorize knowledge segments • Persist in Azure AI Search (vector + semantic index) 4. Retrieval Orchestration • Encode user intent into embedding space • Execute hybrid retrieval (BM25 + ANN search) • Re-rank using similarity scores and metadata constraints 5. Prompt Assembly & Grounding • System instruction + policy constraints + task schema • Inject top-K evidence passages dynamically • Enforce source-bounded generation 6. LLM Reasoning Layer • Invoke GPT (Azure OpenAI) or Claude (Anthropic) • Tune decoding parameters (temperature, top-p, max tokens) • Validate deterministic vs creative response modes 7. Context & State Management • Persist conversational state in Azure Cosmos DB • Apply rolling summarization and relevance pruning • Maintain short-term and long-term memory separation 8. Evaluation & Calibration • Run adversarial, regression, and grounding tests • Measure hallucination rate, retrieval precision, latency • Optimize chunking, ranking heuristics, prompts 9. Productionization & Observability • Deploy via Microsoft Foundry and AKS • Implement distributed tracing, token usage, cost telemetry • Enable human-in-the-loop escalation paths 10. Agentic Capability Expansion • Integrate tool invocation (search, workflow, DB execution) • Add feedback-driven self-correction loops • Implement personalization via behavioral signals The critical steps teams skip: • Step 3 (Semantic Representation): Without proper vectorization, retrieval fails • Step 7 (State Management): Without memory persistence, agents restart every conversation • Step 8 (Evaluation): Without testing, hallucinations go to production My Recommendation: Don't skip steps. Each builds on the previous: • Steps 1-3: Foundation (scope, data, embeddings) • Steps 4-6: Core agent (retrieval, prompts, reasoning) • Steps 7-9: Production readiness (memory, testing, deployment) • Step 10: Advanced capabilities (tools, self-correction) Which step are you currently stuck on? ♻️ Repost this to help your network get started ➕ Follow Anurag(Anu) for more PS: If you found this valuable, join my weekly newsletter where I document the real-world journey of AI transformation. ✉️ Free subscription: https://lnkd.in/exc4upeq

☁️ Every major cloud outage is a reminder that resilience isn’t something you can enable with a checkbox, it’s something you need to explicitly design, test, and adapt as dependencies evolve. A recent “thermal event” in Microsoft Azure’s West Europe region, caused by a cooling system fault triggered hardware shutdowns, took storage units offline, and resulted in broader service disruption across VMs, databases, and Azure Kubernetes Service. Even impacting dependent services in other Availability Zones. Serving as a reminder that zone-redundancy alone isn’t going to be enough when underlying storage fabrics or control-plane dependencies span across availability zones. If your replication strategy still relies on locally-redundant storage (LRS) within a single zone, or even multiple zones in the same region, you're exposed to environmental failures like this. As organizations migrate more critical workloads to the cloud, now is the moment to revisit resilient architecture. Invest in services that span multiple regions to avoid this kind of exposure, and test failover under realistic conditions, so that teams can build muscle-memory and to expose unexpected dependencies. https://lnkd.in/eUsDQ-gH https://lnkd.in/eBz8J3kD

One of the biggest hurdles to mastering Kubernetes isn't just the complexity, it’s the fear of a massive cloud bill at the end of the month. Many beginners stick to local tools like Minikube, but there is no substitute for the experience of working with managed services like Google Kubernetes Engine (GKE). In this latest tutorial, I break down exactly how to spin up a fully functional GKE cluster on Google Cloud for less than the price of a couple of coffees per month. ☕️ Why this approach is a game-changer for your DevOps journey: - GKE Autopilot: Pay only for the pods you run, not for idle infrastructure. - Terraform-Powered: Learn Infrastructure as Code (IaC) by deploying and destroying clusters with a single command. - Security First: Includes best practices like Workload Identity and auto-upgrades right out of the box. - Cost-Saving Hacks What you’ll walk away with: ✅ A repeatable, production-ready Kubernetes setup. ✅ A GitHub starter repo to kickstart your own projects. ✅ The confidence to experiment in a real cloud environment without breaking the bank. If you’re a new GCP user, you can even use your $300 free credits to run this setup entirely for free for 90 days. Watch the full tutorial here: https://lnkd.in/gW4Ec8dN Let’s stop making excuses and start building!

Did you know last week was #AzureAIWeek on the #30DaysOfIA series? The series explores the different tools, technologies and solutions, available to developers to #BuildIntelligentApps. And last week, we focused on building custom copilots, end-to-end, code-first on the Azure AI platform Building Generative AI applications can feel complicated to traditional app developers. But one of the best ways to learn how to construct new solutions - is to deconstruct existing ones to see how they work, then reconstruct them with your own data, scenarios, and decision criteria. Last week Marlene Mhangami and I deconstructed two separate Generative AI solutions through the lens of an end-to-end #GenAIOps app lifecycle. We focused on two key design patterns for modern AI apps - RAG and Agentic AI. With #RetrievalAugmentedGeneration, you ground chat responses in your data. With #MultiAgentCollaboration, you break down complex workflows into specialized single-focus tasks that are executed autonomously to influence the resulting LLM control flow. Check out the kickoff post from the series to learn more, then explore the other posts in the series as we go from prompt to prototype to production code-first with #AzureAIStudio, #Prompty, and #AzureContainerApps. Three Links To Know: 1. Read the #AzureAIWeek of posts https://lnkd.in/eQBWpaY9 2. Contoso Chat Sample https://lnkd.in/e3w87ZyC 3. Contoso Creative Writer Sample https://lnkd.in/e7BaC-Rh Want to get the big picture before you dive in? Here is an illustrated guide to the series that highlights the key takeaways. Happy learning! Devanshi Joshi Kamala Dasika Priyanka Vergadia Amy Kate Boyd David Smith Marc Baiza Patrick Chanezon

99% of teams are overengineering their Kubernetes deployments. They choose the wrong tool and pay for it later lol After managing 100+ Kubernetes clusters and debugging 100s of broken deployments, I’ve seen most teams picking up Helm, Kustomize, or Operators based on popularity, not use case. (1) 𝗜𝗳 𝘆𝗼𝘂’𝗿𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 <10 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 → 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗛𝗲𝗹𝗺 ► Use public charts only for commodities: NGINX, Cert-Manager, Ingress. ► Always fork & freeze charts you rely on. ► Don’t template environment-specific secrets in Helm values. Cost trap: Over-provisioned replicas from Helm defaults = 25–40% hidden spend. Always audit values.yaml. (2) 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗵𝗶𝘁 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗲𝗻𝘃𝗶𝗿𝗼𝗻𝗺𝗲𝗻𝘁𝘀 → 𝗦𝘄𝗶𝘁𝗰𝗵 𝘁𝗼 𝗞𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲 ► Helm breaks when you need deep overlays (staging, perf, prod, blue/green.) ► Kustomize is declarative, GitOps-friendly, and patch-first. ► Use base + overlay patterns to avoid value sprawl. ► If you’re not diffing kustomize build outputs in CI before every push, you will ship misconfigs. Pro tip: Pair Kustomize with ArgoCD for instant visual diffs → you’ll catch 80% of config drift before prod sees it. (3) 𝗦𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀 & 𝗱𝗼𝗺𝗮𝗶𝗻 𝗹𝗼𝗴𝗶𝗰 → 𝗢𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀 𝗼𝗿 𝗯𝘂𝘀𝘁 ► Operators shine when apps manage themselves: DB failovers, cluster autoscaling, sharded messaging queues. ► If your app isn’t managing state reconciliation, an Operator is expensive theatre. But when you need one: Write controllers, don’t hack CRDs. Most “custom” Operators fail because the reconciliation loop isn’t designed for retries at scale. Always isolate Operator RBAC (they’re the #1 privilege escalation vector in clusters.) 𝐌𝐲 𝐇𝐲𝐛𝐫𝐢𝐝 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤 At 50+ services across 3 regions, we use: ► Helm → Install “standard” infra packages fast. ► Kustomize → Layer custom patches per env, tracked in GitOps. ► Operators → Manage stateful apps (DBs, queues, AI pipelines) automatically. Which strategy are you using right now? Helm-first, Kustomize-heavy, or Operator-led?

As organizations move faster into cloud and AI adoption, having the right foundation isn’t just a best practice — it’s a requirement for scale, security, and long-term success. One of the most effective ways to achieve this in Azure is through Landing Zones: a structured, governed, enterprise-ready environment designed to support workloads consistently and securely. But what many teams don’t realize? 👉 The same foundational principles apply to AI workloads. In my latest blog, I cover: 🔹 What Azure Landing Zones are and why you need them 🔹 Key benefits like governance, cost control, scalability, and security 🔹 Core design principles from Microsoft Cloud Adoption Framework 🔹 How Azure AI Landing Zones extend the same methodology to GenAI/ML workloads 🔹 Reference architecture guidance based on Microsoft’s AI Landing Zone implementation If you're building AI at scale, this is how you do it without sacrificing governance or operational control. Read it here 👇 🔗 https://lnkd.in/gkZWgsV3 Let’s keep building AI that’s secure, scalable, and enterprise-ready. 🚀 #Azure #LandingZones #CloudAdoption #AzureAI #EnterpriseArchitecture #CloudGovernance #MicrosoftAzure #AIInnovation #GenerativeAI #CloudSecurity #MLOps #FinOps

What is Kubernetes? Kubernetes is an open-source container orchestration platform originally developed by Google. Think of it as the "operating system" for managing containerized applications across multiple servers. What it does: Automates deployment of containerized applications Scales applications up or down based on demand Manages container health and restarts failed containers Load balances traffic between containers Handles storage, networking, and secrets management Simple analogy: If Docker is like a shipping container for your application, Kubernetes is like the entire shipping port - managing where containers go, ensuring they're running, replacing broken ones, and directing traffic to the right places. Why use it? High availability: If a server fails, K8s moves your apps to healthy servers Scalability: Automatically scale from 1 to 1000s of instances Self-healing: Restarts crashed containers automatically Efficient resource use: Packs containers efficiently across servers

Resilience gaps in #Azure are often buried under “green” dashboards. In one #Dubai government project, we uncovered: • No retry logic in app services • No SLA definition • No chaos testing Using Azure Resilience Patterns + Front Door + Availability Zones, we rebuilt the app stack to survive: • Region failover • Platform service failures • Internal service retries So the Outcome then RTO under 5 minutes for Tier 1 apps & Automated SLA dashboards & Gov-level reliability, by design #Resilience isn’t just HA. It’s fail-proof thinking. #AzureFrontDoor #ResilienceDesign #AppReliability #CloudOps

Alongside building resilient, highly available systems and strengthening security posture, I’ve been exploring a new focus area, optimising cloud costs. Over the last few months, this has led to some clear lessons for me that are worth sharing. 1. Compute planning is the foundation. Standardising on machine families and analysing workload patterns allows you to commit to savings plans or reserved instances. This is often the highest ROI move, delivering big savings without actually making a lot of technical changes. 2. Account structures impact cost. Multiple AWS accounts improve governance and security but make it harder to benefit from bulk discounts. Using consolidated billing and commitment sharing across accounts brings the efficiency back. 3. Kubernetes compute checks are important. Nodes in K8s are often over-provisioned or underutilised. Automated rebalancing tools help, as does smart use of spot instances selected for reliability. On top of this, workload resizing during off hours, reducing CPU and memory when demand is low, delivers direct and recurring savings. 4. Watch for operational leaks. Debug logs on CDNs and load balancers, once useful, often stay enabled long after issues are fixed. They quietly pile up costs until someone takes notice. 5. Right-sizing is a continuous process. Urgent projects often lead to overprovisioned instances for anticipated load that never fully arrives. Monitoring and regular reviews are the only way to keep infrastructure aligned with reality. The real win in cloud cost optimisation comes from treating it as a continuous practice, not a one-off project. Small inefficiencies compound fast, so important to be on the lookout! #CloudCostOptimization #AWS #Kubernetes #DevOps #CloudInfrastructure #RightSizing #WorkloadManagement #SavingsPlans #SpotInstances #CloudEfficiency #TechInsights #CloudOps #CostManagement #CloudBestPractices