AI and ML in Cloud Computing

𝐌𝐨𝐬𝐭 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 𝐭𝐡𝐢𝐧𝐤 𝐀𝐳𝐮𝐫𝐞 𝐀𝐈 𝐢𝐬 𝐣𝐮𝐬𝐭 𝐀𝐳𝐮𝐫𝐞 𝐎𝐩𝐞𝐧𝐀𝐈. That's only one piece of the puzzle. In 2026, building production AI on Azure means understanding the entire AI/ML ecosystem - from data ingestion to LLM deployment and governance. Enterprise AI isn't built with a single service. It's built with an integrated platform. 𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐀𝐳𝐮𝐫𝐞 𝐀𝐈/𝐌𝐋 𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤 (2026 𝐄𝐝𝐢𝐭𝐢𝐨𝐧): ☁️ 1. Compute Layer → Azure ML Compute → Serverless Compute → Azure GPU VMs (ND/NC Series) → Azure Kubernetes Service (AKS) 💡 Power scalable training and inference workloads. 💾 2. Data Storage Layer → Azure Data Lake Storage Gen2 → Azure Cosmos DB → Azure Blob Storage 💡 Every AI system starts with a reliable data foundation. ⚙️ 3. Data Processing & ETL → Azure Databricks → Azure Data Factory (Fabric Data Pipelines) → Azure Event Hubs 💡 Transform, stream, and prepare enterprise data for AI. 🧠 4. ML Training & Experimentation → Azure ML Pipelines (SDK v2) → Microsoft Foundry → Azure ML Prompt Flow → Foundry Agent Service 💡 Build, evaluate, and orchestrate modern AI workflows. 📊 5. Feature Engineering → Azure Databricks Feature Engineering → Azure AI Search → Microsoft Fabric 💡 Better features create better models. 🚀 6. Deployment & Inference → Azure ML Online Endpoints → Azure ML Batch Endpoints → AKS-based Model Deployment → ONNX Runtime 💡 Deploy AI securely at enterprise scale. 🔄 7. Pipelines & Automation → Azure ML Pipelines → GitHub Actions → Azure DevOps Pipelines 💡 Automate everything from training to production. 🤖 8. LLM & Generative AI → Microsoft Foundry → Foundry Models Catalog → Foundry Agent Service 💡 Build enterprise-ready GenAI and Agentic AI applications. 🛡️ 9. Monitoring & Governance → Azure Monitor → Application Insights → Microsoft Purview → Microsoft Entra ID 💡 Enterprise AI requires observability, governance, and security by default. 💻 10. Developer & DevOps Tools → Azure CLI → Bicep → GitHub Copilot → Azure DevOps → GitHub Actions 💡 Accelerate AI delivery with modern DevOps practices. 𝐓𝐡𝐞 𝐛𝐢𝐠𝐠𝐞𝐬𝐭 𝐦𝐢𝐬𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐢𝐨𝐧? ✕ Enterprise AI starts with choosing an LLM. ✓ Enterprise AI starts with building the right platform. Production AI requires far more than models. 𝐈𝐭 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐬: ✅ Compute ✅ Data Engineering ✅ Feature Stores ✅ LLM Orchestration ✅ CI/CD ✅ Monitoring ✅ Governance Because successful AI projects aren't measured by demos. They're measured by reliability, scalability, and business impact. 🚀 In 2026, the engineers creating the most value won't just know Azure AI services. They'll know how to connect them into a production-ready AI platform. That's what separates AI builders from AI architects. Follow Raghavendra Bagalkoti for more on AI 🤖 × Cloud ☁️ × Capital Markets 📊

Software architecture just split into two distinct eras. For decades, traditional systems were built to do one thing flawlessly: execute predefined, deterministic instructions. A user clicks a button. The backend processes the logic. The database updates the state. The system returns an expected, repeatable result. That deterministic DNA is what powered the modern internet—from ERP systems and banking cores to massive e-commerce platforms. But AI systems are fundamentally breaking this paradigm. We are moving away from rigid instructions and shifting toward probabilistic intelligence. AI systems don't just run code—they: Interpret intent rather than just reading inputs. Reason over context instead of following linear paths. Dynamically orchestrate workflows on the fly. Continuously learn from real-time user feedback. Because the logic is changing, the core architecture is being forced to evolve. We are moving away from the classic stack: ➡ [ Frontend → Backend → Database ] And transitioning into a highly interconnected, loop-based web: ➡ [ User/Intent → Orchestrator → Models → Vector DBs → Tools → Memory → Feedback Loops ] This shift is completely redefining the role of hyperscalers. AWS, Azure, and Google Cloud are no longer just infrastructure utilities; they are becoming AI operating environments. The contrast in what we demand from the cloud perfectly highlights this evolution: Traditional Systems Need Cloud For: • Scale-up/scale-out compute • Managed relational databases (RDBMS) • Middleware & structured data pipelines • High availability & multi-AZ disaster recovery • Standard infrastructure governance & security AI-Native Systems Need Cloud For: • Massive GPU/TPU training & inference clusters • Vector databases for embedding retrieval • Multimodal AI services (Speech, Vision, Text) • Ultra-low latency global inference & caching • MLOps, prompt guardrails, & LLM drift monitoring The takeaway? Traditional systems automate tasks. AI systems augment knowledge and drive outcomes. The future isn't about replacing the old stack with the new one. It’s about building the intelligent bridge between them—and we are still in the absolute infancy of this architectural transformation. #AI #CloudComputing #SystemDesign #SoftwareArchitecture #GenerativeAI #LLM #AWS #Azure #GoogleCloud #MachineLearning #AgenticAI #DataEngineering #Infrastructure #Technology #ProductManagement #EnterpriseAI #VectorDatabases #AIArchitecture #DigitalTransformation

Classical machine learning is still powering 80% of the predictions running inside real businesses. Fraud scoring. Demand forecasting. Churn prediction. Pricing models. None of them need a GPT-class model. All of them need a proper architecture. Here is the complete reference for classical ML on Azure, in 10 layers. 𝟭. 𝗗𝗮𝘁𝗮 𝗘𝘀𝘁𝗮𝘁𝗲 Azure Storage, Data Lake, SQL DB, Cosmos DB, Event Hubs, Microsoft Fabric, optional Feature Store. The foundation everything sits on. 𝟮. 𝗔𝗱𝗺𝗶𝗻𝗶𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 & 𝗦𝗲𝘁𝘂𝗽 Project repositories, ML workspaces, datasets, compute resources, user access, monitors. Skip this and governance becomes a nightmare. 𝟯. 𝗜𝗻𝗻𝗲𝗿 𝗟𝗼𝗼𝗽 Tabular data, EDA, feature engineering, model, train, evaluate, responsible AI. The cycle every data scientist lives in. 𝟰. 𝗖𝗜 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 Azure ML model registries via GitHub and Azure DevOps. The bridge between experimentation and production. 𝟱. 𝗢𝘂𝘁𝗲𝗿 𝗟𝗼𝗼𝗽 Continuous development workflows in secure Azure ML workspaces. Where models become services. 𝟲. 𝗦𝘁𝗮𝗴𝗶𝗻𝗴 & 𝗧𝗲𝘀𝘁 Data checks, unit tests, smoke tests, responsible AI, quality assurance, batch and online inference endpoints. The gauntlet every model crosses. 𝟳. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 Batch inference, near real-time inference, Azure Arc, Kubernetes Services, online managed endpoints. The shape of how predictions reach users. 𝟴. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 Application Insights, Azure Monitor, model and data monitoring, infrastructure monitoring. 𝟵. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗥𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Triggers, scheduled retrains, drift detection. The thing junior teams forget until accuracy collapses. 𝟭𝟬. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗥𝗲𝘃𝗶𝗲𝘄 Performance review, availability, latency, alerts. The closing loop. The AI hype gets the headlines. Classical ML quietly runs the economy. Save this. Send it to your MLOps lead. #MachineLearning #Azure #MLOps #DataScience #AI

GCP architecture diagram S 1: Clients What it is: Web browsers, mobile apps, or external services accessing the app. Role: Sends HTTPS requests to the backend APIs. Eg: A user on a mobile app requests product recommendations. S 2: Cloud Run (APIs & Frontend) What it is: Serverless containerized env. Role: Handles stateless requests, API endpoints, and frontend comm. How it works: Receives HTTPS requests from clients. Validates/authenticates requests. Routes requests to the appropriate backend service (GKE microservices or Vertex AI). Key Features: Auto-scaling, pay-per-use, zero infra mgmt. Eg: An API endpoint receives a request for recommended products. S 3: GKE Microservices What it is: Managed K8 cluster hosting microservices. Role: Handles business logic / stateful workloads. Components inside: Pods, Deployments, Services, ConfigMaps, Secrets, HPA, Ingress. How it works: Cloud Run can call GKE microservices for complex operations. Microservices may interact with data stores (BigQuery, Cloud SQL, Firestore). Eg: Order service handles order creation. Catalog service fetches product details. S 4: Vertex AI (ML Models & Prediction) What it is: Fully managed ML platform. Role: Serves ML models for predictions. How it works: Receives API calls (gRPC/HTTP) from Cloud Run or GKE microservices. Generates predictions based on trained ML models. Eg: Predict which products the user is most likely to buy. S 5: Agent Engine (Orchestration & Automation) What it is: Autonomous AI agent framework. Role: Orchestrates multi-step workflows / executes tasks. How it works: Receives inputs from Vertex AI predictions. Calls APIs, fetches or writes data, triggers other services. Eg: After receiving product recommendations from Vertex AI, Agent Engine writes recommendations to a database or triggers an email notification. S 6: Data Layer What it is: Centralized storage for all application data. Components: BigQuery: Analytics and large-scale data processing. Cloud SQL: Relational DB for structured data. Firestore / GCS: NoSQL DB and object storage for unstructured data. Role: Stores input/output for microservices, ML training, and predictions. Example: User data, order history, and product metadata. S 7: Monitoring & Security Layer Components: Cloud Monitoring: Observability, metrics, and alerting. IAM & VPC: Access control/network isolation. Cloud Armor: DDoS protection/security policies. Role: Ensures the system is secure, observable, and resilient. Flow Summary Client sends a request→ HTTPS. Cloud Run API receives request→validates→decides where to route. If logic requires business microservices, Cloud Run calls GKE microservices. If prediction is needed, Cloud Run or GKE calls Vertex AI. Vertex AI returns prediction→passed to Agent Engine. Agent Engine orchestrates tasks→writes results back to Data Layer. Data Layer persists data→can be used for analytics or ML retraining. Monitoring & Security tracks metrics, logs, and enforces policies throughout.

If you look closely at this stack across providers, you’ll notice that AI is just part of the puzzle. I’m not exaggerating when I say, when launching production-grade systems, 80% of the AI challenges continue to be engineering challenges. Selecting which model to work with isn’t even close to being the whole story. To successfully deploy and scale intelligent systems, one needs to understand how to make tradeoffs while evaluating hundreds of services offered by cloud providers like AWS, Google Cloud, and Microsoft Azure Each cloud has its edge; AWS leads in scalability, Google in data innovation, and Microsoft in enterprise integration. Let’s see how they compare across every key layer of the stack : 1.🔸Security & Governance - AWS ensures secure access and monitoring with IAM and GuardDuty. - Google focuses on unified security through Command Center and KMS. - Microsoft leads enterprise defense with Azure Defender and Sentinel. 2.🔸Integration & Automation - AWS automates workflows with Step Functions and Glue. - Google connects systems using Dataflow and Workflows. - Microsoft streamlines operations through Logic Apps and Data Factory. 3.🔸Compute & Infrastructure - AWS delivers scalable compute with EC2, Lambda, and Inferentia chips. - Google uses TPUs and GKE for AI scalability. - Microsoft powers hybrid workloads with Azure VMs and Functions. 4.🔸Data & Analytics - AWS supports data analysis through Redshift and Athena. - Google dominates big data with BigQuery and Looker. - Microsoft combines analytics and visualization via Synapse and Power BI. 5.🔸Edge & Hybrid - AWS offers low-latency AI with Outposts and Wavelength. - Google secures edge processing with GDC and Confidential Computing. - Microsoft extends cloud capabilities using Azure Arc and Stack Edge. 6.🔸Cloud AI Services - AWS offers SageMaker, Comprehend, and Rekognition APIs. - Google provides Vertex AI and Gemini for advanced AI solutions. - Microsoft integrates OpenAI, Cognitive Services, and ML Studio. 7.🔸Agent & Developer Tools - AWS includes Bedrock Agents and CodeWhisperer. - Google enables Gemini and LangChain integrations. - Microsoft supports Copilot Studio and Semantic Kernel. 8.🔸Prototyping & Design Tools - AWS empowers testing with SageMaker Studio Lab. - Google simplifies development using AI Studio and Opal. - Microsoft focuses on no-code creation via Designer and Recognizer Studio. 9.🔸Core Models - AWS relies on Titan and Bedrock models. - Google leads with Gemini. - Microsoft uses Phi, Orca, and Azure OpenAI. Understand how to set up your architecture for scalability, performance, cost, and reliability is a huge advantage, whether via single-cloud, multi-cloud, hybrid, or on-prem. Curious to know how you evaluate tradeoffs from services across these providers to set up your AI systems.

They left GCP for AWS. The result: 25% lower infra cost and 50% less time on ops. Our client runs AI/ML products. GPU cost grew faster than user growth. They had to act. They had already decided to move from GCP to AWS. We used that move to redesign the platform for the next stage: scale GPU workloads, prepare for LLMs, and keep cost in check. We focused on four parts. 1) Smooth migration - We did a mix of lift-and-shift and targeted changes. - Core apps moved first. - Risky parts got extra care. - No big-bang rewrite. - No long downtime. 2) AI/ML on Amazon EKS + GPU EC2 - We built an AI platform on EKS. - GPU-enabled EC2 nodes run models. - Autoscaling reacts to load. - GPU nodes spin up for peaks and sleep when idle. 3) Data layer on Aurora PostgreSQL + S3 - We moved key data to Aurora PostgreSQL. - Cold data lives on S3. - Query speed improved. - Storage cost stays under control. 4) Hybrid GPU strategy - We mixed Spot and On-Demand GPU instances. - Spot lowers cost. - On-Demand keeps reliability. - The system chooses the right mix in real time. The impact:    • 25% lower infrastructure costs   • 40% faster data retrieval   • 30% faster model start time   • 2× faster GPU scaling at peak   • 50% less time on infrastructure managemen Now the customer has a secure, scalable base ready for GenAI and LLM growth, instead of fighting their GPU bill every month. Scaling GenAI is hard, doing it cost-effectively is harder. If that’s your focus, let’s talk. #CloudMigration #AWSforAI #MLOps #EKS

The world of artificial intelligence is moving at lightning speed. At Google Cloud, we’re committed to providing best-in-class infrastructure to power your AI and ML workloads. That's why I’m excited to share ML infrastructure innovation that enable Dataflow to be the best engine for your data parallel ML workloads: ⚡ Performance-Optimized Hardware: We’ve expanded support to include the latest H100 GPUs and TPU v5E/v6E, ensuring your inference workloads have the cutting edge accelerators they need. 🛡️ Greater Availability: With GPU/TPU reservations and Flex-start provisioning (powered by Dynamic Workload Scheduler), you can now queue jobs to start automatically when resources open up—no more manual resubmissions or stockout fears. 🧠 ML-Aware Efficiency: Our streaming engine is getting smarter. GPU-based autoscaling now factors in accelerator usage for better scaling decisions, and Right Fitting allows you to mix and match machine types within a single pipeline to optimize costs. Whether you are powering real-time threat detection like Flashpoint or personalized media experiences like Spotify, Dataflow helps you run high-volume ML workloads at scale. Read the full deep dive here: https://lnkd.in/dpDPsWRm Haakon Ringberg, Riju Kallivalappil, reza rokni, Mustafa Saglam, Danny McCormick, Prateek Duble, Kir Titievsky 🇺🇦, Mehran Nazir, Matt Ryan, Shanmugam Kulandaivel #GoogleCloud #Dataflow #MachineLearning #AI #StreamingAnalytics #DataEngineering

🚀 Starting a New Learning Series on AI/ML Infrastructure & Distributed Systems Most people think building a Machine Learning system is just about training a model. But in reality, the model is only a small part of the entire system. The real challenge begins when we try to move ML from research to production. A production ML system involves multiple components: • Data pipelines that continuously collect and process data • Feature engineering pipelines • Model training and evaluation • Model deployment and serving APIs • Monitoring for model drift and performance issues • Scalable infrastructure to handle millions of requests In large tech companies, ML systems are powered by a combination of: AI/ML Infrastructure Distributed Systems Cloud Computing MLOps Microservices Architecture Over the next few weeks, I’ll be sharing short posts explaining key concepts behind modern ML systems, including: • Feature Stores • Model Serving Systems • Distributed Training • Kubernetes for ML • CI/CD for ML pipelines • Real-world ML system design The goal is to break down complex infrastructure concepts into simple explanations. If you're interested in AI/ML systems, MLOps, or scalable infrastructure, feel free to follow along. #MachineLearning #MLOps #DistributedSystems #AIInfrastructure #CloudComputing #MLSystems