AI and ML in Cloud Computing

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.

𝗔𝗪𝗦 𝗜𝘀 𝗤𝘂𝗶𝗲𝘁𝗹𝘆 𝗕𝗹𝗲𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗜𝗻𝘁𝗼 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 👇 If you're working with Cloud / AWS, you’ve probably noticed something happening lately: AI isn’t just a separate service anymore... it’s being woven into everyday cloud tools. As a cloud learner / professional you just need to understand how these updates are changing the work we do. Let me break it down 👇 🔹 Lambda: Now supports agent-based workflows You can now create AI agents inside AWS Lambda using the new Agent capabilities. This means it can call external APIs, make decisions based on responses, and Execute step-by-step plans. 🔹 CloudWatch: Smarter anomaly detection CloudWatch has added AI-based insights that automatically detect unusual spikes or drops, help explain what caused the change, and reduce the need for manual dashboard digging. 🔹 IAM: AI-generated policy suggestions When creating IAM roles or policies, AWS now offers auto-suggested permissions based on usage, it saves time and reduces the chance of misconfigured access. 🔹 S3: Data prep for AI/ML built-in S3 recently added features like object transformations for model-ready formats, and integrations with SageMaker and Bedrock. Your raw data can be cleaned, structured, and sent to models, all without leaving S3. You don’t need to shift to a new “AI role” to stay relevant, but you do need to notice what’s changing in the tools you already use. Start small, Try the new options, and understand where AI is quietly helping. 💬 Have you tried any of these new AI features in AWS? Let me know in the comments👇 ♻️ Found this helpful? Feel free to repost & share with your network. — 📥 For weekly Cloud learning tips, subscribe to my free Cloudbites newsletter: https://www.cloudbites.ai/ 📚 My AWS Learning Courses: https://zerotocloud.co/ 📹 Watch my weekly YouTube videos: https://lnkd.in/gQ8k29DE #aws #cloud #ai #genai #tech #zerotocloud #techwithlucy

AI development comes with real challenges. Here's a practical overview of three ways AWS AI infrastructure solves common problems developers face when scaling AI projects: accelerating innovation, enhancing security, and optimizing performance. Let's break down the key tools for each: 1️⃣ Accelerate Development with Sustainable Capabilities: • Amazon SageMaker: Build, train, and deploy ML models at scale • Amazon EKS: Run distributed training on GPU-powered instances, deploy with Kubeflow • EC2 Instances:   - Trn1: High-performance, cost-effective for deep learning and generative AI training   - Inf1: Optimized for deep learning inference   - P5: Highest performance GPU-based instances for deep learning and HPC   - G5: High-performance for graphics-intensive ML inference • Capacity Blocks: Reserve GPU instances in EC2 UltraClusters for ML workloads • AWS Neuron: Optimize ML on AWS Trainium and AWS Inferentia 2️⃣ Enhance Security: • AWS Nitro System: Hardware-enhanced security and performance • Nitro Enclaves: Create additional isolation for highly sensitive data • KMS: Create, manage, and control cryptographic keys across your applications 3️⃣ Optimize Performance: • Networking:   - Elastic Fabric Adapter: Ultra-fast networking for distributed AI/ML workloads   - Direct Connect: Create private connections with advanced encryption options   - EC2 UltraClusters: Scale to thousands of GPUs or purpose-built ML accelerators • Storage:   - FSx for Lustre: High-throughput, low-latency file storage   - S3: Retrieve any amount of data with industry-leading scalability and performance   - S3 Express One Zone: High-performance storage ideal for ML inference Want to dive deeper into AI infrastructure? Check out 🔗 https://lnkd.in/erKgAv39 You'll find resources to help you choose the right cloud services for your AI/ML projects, plus opportunities to gain hands-on experience with Amazon SageMaker. What AI challenges are you tackling in your projects? Share your experiences in the comments! 📍 save + share! 👩🏻💻 follow me (Brooke Jamieson) for the latest AWS + AI tips 🏷️  Amazon Web Services (AWS), AWS AI, AWS Developers #AI #AWS #Infrastructure #CloudComputing #LIVideo

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

Most Cloud & DevOps engineers assume these new AI roles don’t concern them - but that’s where they’re wrong.. here’s how the role is evolving: → Model Development Data scientists, ML engineers, and prompt engineers design the models. ↳ But those models still need cloud infra — GPUs, storage, networking, scaling. That’s us. → Model Validation Model validators & ethicists check fairness and accuracy. ↳ DevOps ensures this happens in CI/CD pipelines with automated tests and gated deployments. → Deployment & Integration This is where Cloud/DevOps overlaps the most with AI roles: AI Architect, MLOps Engineer, Software Engineer ↳ We’re the ones putting models into production with Kubernetes, containers, APIs, and load balancers. → Monitoring & Governance Models need more than uptime checks — drift, bias, cost, compliance. ↳ Cloud/DevOps extends into AIOps: metrics, alerts, governance at scale. ⸻ According to the latest WEF report, by 2030 we’ll see 92M jobs lost and 170M new ones created — a churn of 22% of the global workforce. And all of these new jobs? They’re AI-driven. And this shift isn’t limited to labs or big tech. Thanks to the growth of open-source AI tools, agent development is becoming more accessible. So here we are: Cloud & DevOps aren’t being replaced — they’re growing too. We make that infrastructure run securely. If you’re already in this field, your edge is learning how AI workloads run on the infra you already manage. I have shared a whole infra series through my newsletter here - do check it out ( tech5ense.com ) What’s your take on this? • • • I regularly share bite-sized insights on Cloud & DevOps — if you're finding them helpful, hit follow (Vishakha) and feel free to share it so others can learn too!

Most companies talk about “implementing AI”… But very few understand the architecture required to actually make it work. If your AI projects feel slow, siloed, or stuck - the problem usually isn’t the model. It’s the stack behind it. Modern AI isn’t one tool, it’s an ecosystem. You don’t “adopt AI.” You assemble a full architecture that works across apps, data, governance, infrastructure, and automation. Let’s walk through the stack: 1. Enterprise Applications (Buy) AI-powered business apps: copilots, virtual assistants, workflow automation. Examples: ServiceNow, SAP AI. 2. Agentic Platforms (Buy) Platforms to build autonomous, task-executing AI agents. Examples: UiPath AI, CrewAI, LangGraph. 3. Tools, Security & Governance (Buy) IAM, guardrails, monitoring, compliance, risk controls. Examples: Purview, GuardDuty, Keycloak. 4. RAG & Integration Layer Connect enterprise data to LLMs using pipelines & retrieval systems. Examples: Pinecone, LlamaIndex, LangChain. 5. Foundation Models Open, closed, and fine-tuned LLMs + multimodal models. Examples: GPT-4.1, Gemini, Mistral, Llama. 6. Data & Databases Vector stores, data warehouses, real-time infrastructure. Examples: Snowflake, BigQuery, MongoDB. 7. Observability & Evaluation Track drift, latency, accuracy, hallucinations, cost. Examples: Arize AI, LangSmith, Giskard. 8. Public Cloud AI GPU/TPU infrastructure, hosting, training pipelines. Examples: Azure ML, AWS Bedrock, Vertex AI. 9. Private Cloud & On-Prem AI Self-hosted models, secure inference, GPU clusters. Examples: NVIDIA DGX, OpenShift AI. 10. Hardware Infrastructure AI chips, compute fabric, networking. Examples: NVIDIA H100, AMD MI300, TPU v5. 11. Edge AI On-device inference for ultra-low-latency automation. Examples: Jetson, Apple Neural Engine. AI success in 2026 won’t depend on who has the best model… But on who has the best architecture - end-to-end, governed, scalable, and integrated. Which layer of this stack do you think companies struggle with the most? Follow Vaibhav Aggarwal For More Such AI Insights!!

🤝🏻️ Bringing it all together: Giving AI Agents the power of ML Models with Amazon SageMaker AI and Amazon Bedrock AgentCore 🚀 Excited to share my latest blog post where I dive deep into combining the power of ML models with AI Agents, and show you how to: ⭐️ Deploy ML models using Amazon SageMaker AI endpoints ⭐️ Leverage Amazon Bedrock AgentCore Gateway with AWS API Smithy Models ⭐️ Create custom MCP servers with Amazon Bedrock AgentCore Runtime ⭐️ Build intelligent AI Agents that can make ML-powered predictions 💡 Whether you're looking to scale your ML operations or enhance your AI Agents with predictive capabilities, this guide shows you two powerful approaches to achieve it. 🛠️ Complete with code examples and step-by-step instructions, you'll learn how to turn your ML models into powerful tools that AI Agents can leverage for real-world applications like demand forecasting. 🔗 Check out the full article and code repository in the comments below 👇🏻️ #AWS #MachineLearning #ArtificialIntelligence #CloudComputing #Innovation #TechNews #AWSCommunity #AIAgents #SageMaker #Bedrock

𝗧𝗟;𝗗𝗥: While GenAI is the latest rage, 𝗔𝗜 𝗶𝘀 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰 & enterprises that have a 𝗰𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗲 𝘃𝗶𝗲𝘄 are being more successful with AI. Do not use the 𝗚𝗲𝗻𝗔𝗜 𝗵𝗮𝗺𝗺𝗲𝗿 all the time! The field of AI has so many branches, I will focus on the major ones. At the top level AI can be split into two branches 𝟭/𝗦𝘆𝗺𝗯𝗼𝗹𝗶𝗰 𝗔𝗜 (𝟭𝗮/𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 (AR) & 𝟭𝗯/𝗘𝘅𝗽𝗲𝗿𝘁/𝗥𝘂𝗹𝗲 𝗦𝘆𝘀𝘁𝗲𝗺𝘀) & 𝟮/𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (ML) ML has two major branches 𝟮𝗮/𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗟 & 𝟮𝗯/𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀 (NNs). - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗟 usually refers to Decision Trees, Random Forests, Linear regression, SVMs, clustering etc. They are based on various mathematical principles & are often used for tasks such as classification, regression, clustering. They are effective with structured data or when interpretability is important. - 𝗡𝗡 𝗯𝗮𝘀𝗲𝗱 𝗠𝗟 has two major branches 𝟮𝗯𝟭/𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝟮𝗯𝟮/𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴. Shallow uses networks with under 3-4 layers of neurons & thousands of parameters & Deep has hundreds of layers of neurons & billions of parameters. -- 𝗦𝗵𝗮𝗹𝗹𝗼𝘄 𝗡𝗡 𝗺𝗼𝗱𝗲𝗹𝘀 are great for Predictive use cases with small data sets, linear data relationships & low compute requirements (like maybe the Edge). They are not the ideal for GenAI. -- 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 have become really popular in the last 10-12 years with improved access to data, compute (GPU) & are great for Predictive & GenAI use cases. Deep learning powered Predictive AI is great for large data sets with complex, non-linear data relationships.   (𝙏𝙝𝙚 𝙏𝙧𝙖𝙣𝙨𝙛𝙤𝙧𝙢𝙚𝙧 𝙖𝙧𝙘𝙝𝙞𝙩𝙚𝙘𝙩𝙪𝙧𝙚 𝙪𝙨𝙚𝙙 𝙞𝙣 𝙂𝙚𝙣𝘼𝙄 𝙞𝙨 𝙢𝙪𝙡𝙩𝙞𝙥𝙡𝙚 𝙡𝙖𝙮𝙚𝙧𝙨 𝙤𝙛 𝘿𝙚𝙚𝙥 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙉𝙉𝙨) Customers are 𝗰𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝘃𝗮𝗿𝗶𝗼𝘂𝘀 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗲𝘀. For example, Nasdaq using Amazon Web Services (AWS) (https://bit.ly/3XCGwFC) uses Predictive AI to identify fraud & then uses GenAI to ease the laborious creation of fraud document. Other examples include 𝗔𝗥 𝗯𝗲𝗶𝗻𝗴 𝗰𝗼𝗺𝗯𝗶𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗚𝗲𝗻𝗔𝗜 to mathematically validate generated code (https://lnkd.in/e73Rk4NJ) & throw out bad code before a developer even sees it. Composite AI was used to win Math Olympiads (https://bit.ly/4dSkNzg - LEAN by Leonardo de Moura, 𝗮𝗻 𝗔𝗥 𝗺𝗼𝗱𝗲𝗹 𝘂𝘀𝗲𝗱 𝘄𝗶𝘁𝗵 deep) & there will be more!     The above framework is not exhaustive & sure folks will find gaps but wanted to highlight the key point that there is more to AI than GenAI & in many cases 𝗚𝗲𝗻 𝗔𝗜 𝗶𝘀 𝗽𝗿𝗼𝗯𝗮𝗯𝗹𝘆 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 & 𝗲𝘃𝗲𝗻 𝗲𝗿𝗿𝗼𝗿 𝗽𝗿𝗼𝗻𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝘁𝗼 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝗮 𝗽𝗿𝗼𝗯𝗹𝗲𝗺! 𝗔𝗰𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗧𝗢𝘀, 𝗖𝗔𝗜𝗢𝘀 & 𝗖𝗜𝗢𝘀: Invest in looking at AI holistically & have an AI (not just GenAI) platform. Question the overuse of GenAI in the enterprise. 

AI and Cloud: Can They Lift Each Other Up? Cloud and AI are deeply interconnected, each essential to the other's success. Here are just a few examples: 1. AI Leverages Cloud: -- AI as a Service: Many AI solutions are delivered as SaaS or deployed on cloud platforms. -- AI in the Cloud: Cloud offers the easiest way to deploy AI, making robust cloud security solutions essential for protecting AI-powered applications and data. 2. Cloud Benefits from AI: -- Security Posture: Generative AI is showing promising results in simplifying cloud security posture management via security baselines automation. -- Compliance Management: Given the Cloud's well-defined language, AI assists in effectively mapping it to compliance frameworks. 3. Centers of Excellence (COEs): Both AI and Cloud are breaking down traditional organizational silos. AI and Cloud COEs have similar mission and structures. Both coordinate strategies and integration to drive innovation. Unifying the Cloud-AI processes and lessons learned allows organizations to accelerate digital transformation and achieve significant business advantages. Excited to see the joint AI-Cloud journey ahead! #Cloud #AI #CloudSecurity #AISecurity