Software Engineering Cloud Computing

If you’re building a career around AI and Cloud infrastructure ~ this roadmap will help map the journey. It breaks down the Cloud AI Engineer role into 12 focused stages: – Build a strong foundation in cloud platforms and Linux (it’s everywhere), and understand networking, storage, and core infrastructure concepts – Practice containerization and orchestration with Docker and Kubernetes to run scalable AI workloads – Provision infrastructure using Infrastructure as Code (Terraform, Ansible, cloud-native tools) and CI/CD pipelines – Understand AI/ML fundamentals including model architectures, training vs inference workflows, and distributed training concepts – Get familiar with GPU computing, CUDA, and NVIDIA GPU architectures used for AI workloads – Know how high-performance networking works for AI clusters using RDMA, GPUDirect, and optimized network fabrics – Know how to manage AI storage systems including object storage, NVMe, and parallel file systems for large datasets (and why storage can become a bottleneck) – Understand how to run AI workloads on Kubernetes with GPU scheduling, Kubeflow, and ML job orchestration – Learn how to optimize and deploy AI inference pipelines using TensorRT, Triton, batching, and model optimization techniques – Know how to build distributed training infrastructure for large models using NCCL, NVLink, and multi-node GPU clusters – Implement monitoring and observability for AI systems with GPU metrics, tracing, and performance profiling – Operate production AI systems with multi-cluster architectures, disaster recovery, and enterprise-scale AI infrastructure So if you’re building AI models but don’t understand the infrastructure behind them ~ this roadmap helps connect the dots. Resources in the comments below 👇 Hope this helps clarify the systems and skills behind the role. • • • If you found this insightful, feel free to share it so others can learn from it too.

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

𝐋𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤 𝐚𝐛𝐨𝐮𝐭 𝐂𝐥𝐨𝐮𝐝 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 🛠️ 𝐓𝐢𝐩𝐬 𝐟𝐨𝐫 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐞𝐬: 💡 Understand your workload pattern: Read-heavy? Write-heavy? Latency-sensitive? 💡 Pick storage/network options based on IOPS vs Throughput: EBS gp3 vs io2, or GCP SSD vs balanced disk. 💡 Set autoscaling policies: Scale on metrics like CPU, memory, latency. 💡 Use monitoring tools. Imagine you’re running a logistics company. You manage warehouses↔️storage, delivery trucks↔️networks and orders↔️requests. Your success depends on how efficiently you can move goods. 🛠️𝐈𝐎𝐏𝐒 = 𝐍𝐮𝐦𝐛𝐞𝐫 𝐨𝐟 𝐎𝐫𝐝𝐞𝐫𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐝 𝐩𝐞𝐫 𝐌𝐢𝐧𝐮𝐭𝐞 How many packages your warehouse staff can handle every minute. 💡 In the cloud: Choose high-IOPS storage (like AWS io2 or GCP SSD) if your app handles lots of small reads/writes, like a database or messaging queue. 🛠️ 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 = 𝐖𝐞𝐢𝐠𝐡𝐭 𝐨𝐟 𝐆𝐨𝐨𝐝𝐬 𝐌𝐨𝐯𝐞𝐝 𝐩𝐞𝐫 𝐌𝐢𝐧𝐮𝐭𝐞 How many tons of packages your trucks can deliver per minute. One truck carrying 10 large items = high throughput, even if it’s fewer deliveries. 💡 In the cloud: For video streaming etc. go for high-throughput volumes (like AWS st1 or gp3 with tuned throughput). 🛠️ 𝐋𝐚𝐭𝐞𝐧𝐜𝐲 = 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐓𝐢𝐦𝐞 𝐩𝐞𝐫 𝐏𝐚𝐜𝐤𝐚𝐠𝐞 Packages need to 𝐚𝐫𝐫𝐢𝐯𝐞 𝐨𝐧 𝐭𝐢𝐦𝐞. Even small delays can frustrate customers if they expect fast service. 💡 Use low-latency solutions (fast disks, caching) for real-time systems like payment processing. 🛠️ 𝐐𝐮𝐞𝐮𝐞 𝐃𝐞𝐩𝐭𝐡 = 𝐏𝐚𝐜𝐤𝐚𝐠𝐞𝐬 𝐖𝐚𝐢𝐭𝐢𝐧𝐠 𝐢𝐧 𝐋𝐢𝐧𝐞 Too many packages waiting = your warehouse is overwhelmed. 💡 Monitor queue depth (especially with databases, message queues, or autoscaling systems) to ensure your infrastructure can keep up. 🛠️ 𝐂𝐚𝐜𝐡𝐞 𝐇𝐢𝐭 𝐑𝐚𝐭𝐢𝐨 = 𝐔𝐬𝐢𝐧𝐠 𝐏𝐫𝐞-𝐩𝐚𝐜𝐤𝐞𝐝 𝐁𝐨𝐱𝐞𝐬 Like grabbing pre-packed, ready-to-ship boxes vs. assembling every order from scratch. High cache hit = fast delivery and lower warehouse load. 💡 In the cloud: Use Redis/Memcached, CloudFront, or Cloud CDN to reduce backend pressure and save costs. 🛠️ 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐓𝐡𝐫𝐨𝐮𝐠𝐡𝐩𝐮𝐭 = 𝐇𝐢𝐠𝐡𝐰𝐚𝐲 𝐒𝐩𝐞𝐞𝐝 & 𝐂𝐚𝐩𝐚𝐜𝐢𝐭𝐲 Your delivery trucks need wide roads and smooth traffic to reach their destination fast. Narrow roads = congestion, even if your trucks are fast. 💡 Choose instances or services with proper network bandwidth for microservices, real-time communication, or multi-region sync. 🛠️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Speed, capacity, and efficiency must all work together. In terms of cloud, 𝐦𝐞𝐭𝐫𝐢𝐜𝐬 = 𝐨𝐩𝐬 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝. Monitoring when to add trucks, optimize routes, or expand warehouses—without wasting money. #CloudCostOptimization #CloudSavings #tech #techblogs #engineers #developers #costops

Many beginners start cloud with AWS, Azure, or GCP, but only at the console level. Clicking around in the UI will not make you cloud-ready. Cloud is about understanding concepts, not memorizing services. Some fundamentals to master before diving deep: - How virtualization evolved into cloud - Difference between IaaS, PaaS, and SaaS - Regions, zones, and availability concepts - Shared responsibility model (where your role ends, where the provider’s starts) - Basics of networking in the cloud (VPCs, subnets, security groups) - Cost management: why the cloud can be cheap or very expensive Before mastering Lambda or Kubernetes on cloud - ask: 👉 Do I understand the building blocks that make the cloud reliable and scalable? Like this post if you’re learning cloud the right way. #DevOps #CloudComputing #AWS #Azure #GCP #DevOpsLearning #CareerGrowth #DevOpsEngineers

You’ve just joined a mid-size company as a GRC Coordinator. Your manager asks you to support an upcoming vendor risk review. One of the company’s key third-party platforms experienced a minor outage last month. Leadership now wants better visibility into vendor risk before renewing the contract. You begin by checking if the vendor has submitted any recent documentation. You locate an outdated security questionnaire from over two years ago. It mentions a legacy data center setup, but the vendor now operates entirely in the cloud. That discrepancy is a red flag. You reach out to the vendor, letting them know your company is refreshing its records. You send over a short but targeted questionnaire with updated questions about incident response, encryption practices, and subcontractors. You also ask for any available certifications, like a SOC 2 report or ISO 27001. Internally, you check with Procurement and IT to understand the vendor’s role. It turns out this vendor supports customer login and account access, which means their reliability directly impacts the user experience. You mark them as high impact and recommend that they be monitored more closely. You update your team’s vendor risk tracker with the new responses and supporting files. In your notes, you recommend moving this vendor to the quarterly reassessment schedule instead of annual, based on their business function and the recency of the outage. 1. You identified a risk based on outdated information. 2. You improved visibility by asking for updated documentation. 3. You flagged a business-critical system and recommended changes to the review cadence. 4. You kept your company informed and protected with practical follow-up. You don’t have to be a vendor risk expert to add value. You just need to ask the right questions, connect with the right people, and document what you find clearly.

Most vendor management programs fail for the same reason. And it's not the vendors. In every audit, I always start with the 5 Whys. • Why isn't the vendor delivering? Performance keeps slipping. • Why is performance slipping? Nobody set clear expectations. • Why are there no clear expectations? There's no structured governance. • Why is there no governance? Nobody owns the relationship inside IT. • Why is there no owner? Vendor management has no strategy behind it. Five layers down, the vendor disappears. What's left is your own structure. That's why I built SGAR: S → Strategy (segment the vendors that are important) G → Governance (regular touch-points and clear accountability) A → Assessment (expectations vendors can deliver against) R → Relationship (proactive, not reactive firefighting) The 5 Whys exposes what's broken. SGAR shows you how to fix it. It's faster to blame the vendor. If you want to fix it, audit the structure. P.S. If you ran the 5 Whys on your worst-performing vendor today, where would you stop?

Every app you use daily runs on the same 20 building blocks. Most engineers only know half of them. This quick guide breaks down the essential infrastructure pieces behind real-world software systems, helping you understand how production architectures actually function beyond just writing code. Key Concepts Covered • Load Balancers - distribute incoming traffic across servers for stability • API Gateway - central entry point for routing, security, and control • Application Servers - execute backend logic and handle user requests • Microservices - independent services enabling flexible scaling and deployment • Auto Scaling - automatically adjust resources based on system demand • Object, Block & File Storage - store data optimized for different workloads • CDN - deliver content globally with reduced latency and faster performance • DNS - route users to the nearest and healthiest infrastructure endpoint • Message Queues - enable asynchronous communication between services • Event Streams - process continuous real-time system events • Cache (Redis) - speed up applications by reducing database queries • Search Engines - power fast search, indexing, and discovery experiences • Stream Processors - analyze live data flows instantly • SQL Databases - ensure structured transactions and strong consistency • NoSQL Databases - support massive scale and flexible schemas • Data Warehouses - enable analytics and large-scale reporting workloads • Analytics Engines - transform raw data into business insights • Session Stores - maintain user sessions across distributed systems • Monitoring & Logging - observe performance and troubleshoot failures • Distributed Tracing & Service Discovery - track requests across services dynamically Key takeaway: Great applications aren’t scalable because of better code alone - They scale because of well-designed system architecture layers working together. Save this guide if you’re learning System Design, Backend Engineering, or Cloud Architecture.