How to Build Resilient AI Systems with Azure Functions and AKS

📊 Grafana + AI = a new era of IT observability! In our latest article, we break down how Grafana leverages AI in real-world use cases: ✅ Adaptive Metrics — cut storage costs by up to 35% ✅ Sift — AI assistant for faster incident analysis and log investigation ✅ Monitoring of LLMs and vector databases ✅ Automation of daily tasks, flame graph analysis, GPU & AI infra monitoring 👉 Read the full article and discover how AI is changing the way we monitor systems: 🔗 https://lnkd.in/dE3xYdbA Need help implementing Grafana or integrating AI into your observability stack? Reach out — we’re happy to help! #Grafana #Observability #AIOps #DevOps #Monitoring #MachineLearning #LLM #Cloud #ITOps #OpenSource Grafana Labs

𝐀𝐈 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐛𝐞 𝐬𝐦𝐚𝐫𝐭, 𝐢𝐭 𝐧𝐞𝐞𝐝𝐬 𝐭𝐨 𝐛𝐞 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞. ⚡⁣ One of the biggest challenges with GenAI today isn’t the models themselves, but what happens when:⁣ o  A provider suddenly goes down⁣ o  Latency spikes during peak usage⁣ o  Costs spiral with every extra query⁣ ⁣ That’s where smart gateways come in. Think of them as the air traffic control for AI, automatically:⁣ ✅ Rerouting requests when a provider struggles⁣ ✅ Balancing quality vs. cost in real time⁣ ✅ Keeping systems running without teams firefighting at 2 AM⁣ ⁣ What’s exciting is how both enterprises and the open source ecosystem are tackling this:⁣ o  𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦𝐬 like AWS Bedrock, Azure AI Studio, Google Vertex AI → managed resiliency & integrations⁣ o  𝐀𝐏𝐈 𝐠𝐚𝐭𝐞𝐰𝐚𝐲𝐬 (Kong, Tyk) + observability tools (Datadog, Prometheus, OpenTelemetry) → health checks, circuit breakers, real time insights⁣ o  𝐎𝐩𝐞𝐧 𝐬𝐨𝐮𝐫𝐜𝐞 𝐬𝐭𝐚𝐜𝐤𝐬 like LiteLLM, LangChain, BentoML → multi model orchestration with real flexibility⁣ ⁣ 👉 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Resilience is becoming just as important as intelligence in GenAI.⁣ ⁣ Curious to know how are you (or your teams) approaching routing, failover, and cost optimization in your AI stack?⁣ ⁣ #GenAI #AIInfrastructure #AIGateways #AIOperations #MLOps #LangChain #AWS #AzureAI #VertexAI #OpenSourceAI⁣

The future of infrastructure is here, and it's intelligent! 🚀 As our systems become increasingly complex, traditional monitoring just can't keep up. That's where AI-powered observability becomes a game-changer. Imagine interacting with your logs, metrics, and traces using natural language, getting instant insights, and even building dashboards on the fly. Tools like Grafana Assistant, integrating advanced LLMs into Grafana Cloud, are making this a reality. For businesses relying on high-performance MCP servers and distributed systems, AI-driven observability means: • 📊 Predictive Outlier Detection: Catch issues before they impact users. • ⏱️ Faster Failure Triage: Pinpoint root causes with unprecedented speed. • ⚙️ Smarter Resource Optimization: Ensure your systems are always running efficiently. • 🗣️ Natural Language Interaction: Empowering everyone, from engineers to non-technical users, to understand complex data. This isn't just about monitoring; it's about transforming how teams debug, investigate incidents, and scale operations. Simplifying complexity and saving valuable time is now within reach. #observability #AI #AIOps #devops #grafana #monitoring #cloudnative #futureoftech #LLMs #GrafanaAssistant

🌟 Building Real-World GenAI Systems: Key Lessons Learned Over the past year, I’ve had the chance to design and deploy full-stack Generative AI applications using LangChain, GPT-4, and vector databases across cloud platforms like AWS and Azure. A few key takeaways from the journey: 🔹 RAG > Raw LLMs – Retrieval-augmented generation cut hallucinations by ~28% and improved trust in enterprise settings. 🔹 Streaming UIs matter – Token-level streaming over WebSockets transformed the user experience, making AI interactions feel natural. 🔹 Ops is everything – Containerization + CI/CD on AKS/EKS and monitoring with Prometheus/Grafana ensured these systems scaled reliably. 🔹 Responsible AI – Incorporating prompt safety, data privacy, and audit logging early saved a lot of downstream headaches. GenAI isn’t just about smarter models—it’s about building end-to-end systems that are accurate, scalable, and responsible. Curious to hear: what’s been your biggest challenge (or breakthrough) when deploying GenAI in production? 📩 Reach me anytime at jaganadari0825@gmail.com 📞 +1 (806) 429-2130 #GenAI #MLOps #LangChain #RAG #AIagents #AWS #Azure

🚀 AWS Bedrock AgentCore: Supercharging AI Agents! 🤖⚡ Building intelligent AI agents just got easier with AgentCore, AWS’s new serverless runtime built for the agentic AI era. Here’s why it’s a game-changer 👇 🔹 Serverless & Secure → Run agents without managing infrastructure, with full isolation & checkpointing. 🔹 Long-Running Tasks → Support for tasks up to 8 hours with automatic recovery. 🔹 Any Model, Any Framework → Use Bedrock models or external ones with LangGraph, CrewAI, Strands Agents, etc. 🔹 Persistent Memory → Agents can remember context across sessions for smarter workflows. 🔹 Enterprise Ready → Identity integration (Okta, Cognito), observability (CloudWatch, OpenTelemetry), and more. 💡 AgentCore isn’t just about building agents—it’s about making them production-ready at scale. 👉 What use case would you build with Bedrock AgentCore first—customer support, automation, or multi-agent systems? #AWS #Bedrock #AgentCore #Cloud #AI #DevOps

Kubernetes didn't rise as the orchestration layer for hybrid clouds by chance, it was earned through years of battle-tested deployments and continuous improvement. In this InfoWorld Tech Forum article, Yellowbrick CTO Mark Cusack breaks down how Kubernetes has emerged as the critical enabler in the shift of how enterprises approach infrastructure. https://bit.ly/4nbgR1g #Kubernetes #HybridCloud #AI #Database

Part 2: Backbone built — time to switch on super-vision. While the world discusses AI and AI infrastructure, today we need to focus on the critical lens that keeps it performing at its peak: "Observability". Even the strongest AI stack can under-perform without visibility. Observability transforms signals into decisions, providing real-time, end-to-end visibility to identify issues early, resolve them quickly, and maintain optimal performance. Why Observability Matters Across the Stack: **Compute**: Catch CPU/GPU saturation, memory pressure, and queue backlogs before they impact training or inference. **Storage**: Prevent pipeline stalls by monitoring throughput, IOPS, and latency. **Networking**: Detect jitter, packet loss, and P99 spikes that disrupt distributed workloads. **Frameworks & Platforms**: Monitor model accuracy, error rates, and pipeline timing for quicker root cause analysis. **Cloud & Hybrid**: Gain visibility across on-prem, cloud, and edge to optimize cost, scale, and compliance. **AppStack**: Connect traces and SLOs to user experience and business impact. What Good Looks Like: **Baseline the Stack**: - Compute: Utilization, queue depth, batch throughput - Storage: Read/write latency, IOPS, cache hit rate - Network: P95/P99, packet loss, retries - Frameworks: Accuracy drift, error rate, stage timings - Cloud/Hybrid: Cost per workload, auto-scale events, quotas - AppStack: Traces, RUM, SLOs, and error budgets **Instrument Models**: - Control cost & noise: - Data efficiency: Tail-based sampling, log filtering, hot/cold retention tiers Quick Takeaway: Infrastructure provides power, while observability offers clarity. Together, they enable trustworthy, scalable AI and enhance user satisfaction. How are you ensuring visibility across every layer of your AI stack, especially the AppStack where customers feel it? Quick takeaway Infrastructure gives you power. Observability gives you clarity. Together, you get trustworthy, scalable AI — and happier users. Question How are you ensuring visibility across every layer of your AI stack — especially the AppStack where customers feel it? #AIInfrastructure #Observability #Cloud #HybridCloud #DigitalTransformation #MLOps #PlatformEngineering #OpenTelemetry #FinOps

🚀 Recently had the opportunity to work on deploying Azure AI Foundry in an enterprise environment. Our setup included: • Terraform-driven deployment of AI Foundry and dependencies • Private endpoints for Azure AI Search, Cosmos DB, Storage Account, and Key Vault • Parameterization for reusable configurations across environments • Role assignments & governance aligned with enterprise policies Key takeaways: ✅ Networking & private access were critical to meet security standards ✅ Parameterization made deployments consistent across test & production subscriptions ✅ Collaboration with cross-functional teams (Cloud Factory, CSA, customer stakeholders) ensured alignment with governance requirements This project reinforced how AI Foundry acts as a hub to orchestrate OpenAI models, AI Search, and enterprise data securely and at scale. #Azure #AI #AzureAIFoundry #Terraform #CloudSecurity #EnterpriseAI

Discover the key trends shaping backend development in 2025: serverless computing, microservices, AI integration, and more. • Stay ahead with these insights. • #backenddevelopment #serverlesscomputing #AIintegration Full Post: https://lnkd.in/gCeexww3

Fascinating stat I came across: Companies using cloud-native AI architectures report 3x faster time-to-market compared to those building on-premises. But here's what the numbers don't tell you - it's not just about speed. It's about a fundamental shift in how we think about intelligent systems. I've been working with teams across industries, and the pattern is clear: ❌ Old approach: Build massive, monolithic AI platforms ✅ New approach: Compose microservices that happen to be intelligent The winners aren't necessarily the ones with the best data scientists. They're the ones who've figured out event-driven architectures, proper container orchestration, and monitoring that goes beyond traditional application metrics. Infrastructure as Code for AI workloads? Non-negotiable. Model versioning and registries? Table stakes. Continuous training pipelines? Essential for staying competitive. The gap between experimental AI and production AI isn't technical knowledge - it's architectural thinking. Anyone else seeing this shift in their organizations? Read more: https://lnkd.in/gUHd2ast #CloudAI #MLOps #Microservices #TechTransformation