Software Engineering Cloud Computing

The first time I heard "multi-cloud", it sounded simple. Use AWS for this. Google Cloud for that. Azure for something else. Best tool for each job. Easy. Then I actually tried to make it work. Suddenly I was dealing with: ‼️ Three different credential systems ‼️ Cross-cloud networking that nobody talks about ‼️ Infrastructure I had to maintain in two or three places at once And a cloud bill that made no sense. Here's what you should know: Netflix, Spotify, and Uber use multi-cloud. But not because it's simple. Because they figured out the right architecture to make it manageable. So I made a full video breaking down exactly how multi-cloud works in practice: https://lnkd.in/dbchEztk Not the theory. The real problems. The real solutions. And a live demo deploying across AWS, Azure and GCP. If you're working with cloud infrastructure — or want to — this one is worth your time 🙂 💬 Are you already working with multi-cloud at your company? Curious to hear where most teams actually are with this.

Netflix once asked a terrifying question: “What happens if our entire database disappears?” - Not a table. - Not a shard. The entire database. To test this, they built a tool called Chaos Monkey, which randomly kills servers. Then they went further and built: - Chaos Gorilla, which simulates losing an entire Availability Zone - Chaos Kong, which simulates losing an entire AWS region These tools intentionally destroy large parts of Netflix infrastructure to ensure the system can survive the worst possible event. When they first ran Chaos Kong internally, dozens of microservices failed. - Fallbacks were missing. - Cross region replication did not handle traffic properly. - Caches did not warm up fast enough. But instead of hiding it, Netflix made this part of their routine engineering practice. That is how their resilience was built: - Multi region active active architectures - Cross region failovers - Stateless services - Data replication with conflict resolution - Region isolation testing All of these are real Netflix engineering strategies, documented openly in their tech blogs and conference talks. You do not build reliability by hoping things will not break. You build reliability by intentionally breaking them in controlled ways.

🚨NSA Releases Guidance on Hybrid and Multi-Cloud Environments🚨 The National Security Agency (NSA) recently published an important Cybersecurity Information Sheet (CSI): "Account for Complexities Introduced by Hybrid Cloud and Multi-Cloud Environments." As organizations increasingly adopt hybrid and multi-cloud strategies to enhance flexibility and scalability, understanding the complexities of these environments is crucial for securing digital assets. This CSI provides a comprehensive overview of the unique challenges presented by hybrid and multi-cloud setups. Key Insights Include: 🛠️ Operational Complexities: Addressing the knowledge and skill gaps that arise from managing diverse cloud environments and the potential for security gaps due to operational siloes. 🔗 Network Protections: Implementing Zero Trust principles to minimize data flows and secure communications across cloud environments. 🔑 Identity and Access Management (IAM): Ensuring robust identity management and access control across cloud platforms, adhering to the principle of least privilege. 📊 Logging and Monitoring: Centralizing log management for improved visibility and threat detection across hybrid and multi-cloud infrastructures. 🚑 Disaster Recovery: Utilizing multi-cloud strategies to ensure redundancy and resilience, facilitating rapid recovery from outages or cyber incidents. 📜 Compliance: Applying policy as code to ensure uniform security and compliance practices across all cloud environments. The guide also emphasizes the strategic use of Infrastructure as Code (IaC) to streamline cloud deployments and the importance of continuous education to keep pace with evolving cloud technologies. As organizations navigate the complexities of hybrid and multi-cloud strategies, this CSI provides valuable insights into securing cloud infrastructures against the backdrop of increasing cyber threats. Embracing these practices not only fortifies defenses but also ensures a scalable, compliant, and efficient cloud ecosystem. Read NSA's full guidance here: https://lnkd.in/eFfCSq5R #cybersecurity #innovation #ZeroTrust #cloudcomputing #programming #future #bigdata #softwareengineering

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

The trend towards multi-cloud interoperability transforms modern IT infrastructures, allowing organizations to leverage flexibility, cost efficiency, and resilience by ensuring seamless integration across different cloud environments. Achieving effective multi-cloud interoperability relies on essential design principles prioritizing flexibility and adaptability. Cloud-agnostic coding minimizes dependencies on specific platforms, reducing lock-in risks. The microservices-based design allows applications to remain modular and scalable, making them easier to manage and integrate across diverse cloud providers. Automation, by reducing manual intervention, lowers complexity, enhances efficiency, and improves system resilience. Exposing APIs by default standardizes communication and ensures seamless interactions between components. A robust CI/CD pipeline enhances reliability and repeatability, enabling continuous updates and adaptations that meet evolving business needs. #CloudComputing #multicloud

This resume got interviews at Amazon, Elevance Health, Cognizant, Autodesk & here are the reasons why: Strategic Information Hierarchy: - Education First: Master's student (graduated May 2025), placing education at the top is a strategic move. It immediately highlights their advanced qualifications and high GPA (4.00). - Clear Sections: Bolded headers like EDUCATION, SKILLS, and WORK EXPERIENCE create a clean, organized layout that is easy for recruiters to navigate quickly. - Consistent Formatting: The consistent placement of dates and locations on the right-hand side makes the timeline of their experience simple to follow. Quantifiable Achievements Everywhere: Metrics are used effectively throughout the resume to demonstrate tangible impact. This moves beyond simply listing duties and shows concrete results. "Boosted performance by 62% and cut test failures by 78%" "Developed a C++ module handling 1.5M+ events/sec" "Structured SQL databases to efficiently process 1TB+ of input voice data monthly" "Applied Elastic Autoscaling EC2 instances... supporting 10,000+ concurrent users" "Fortified hybrid cloud infrastructure by 30%" "Upgraded Natural Language Processing models... boosting overall accuracy by 20%" Action-Oriented & Tech-Specific Descriptions: - Each bullet point begins with a strong action verb, such as "Engineered," "Deployed," "Containerized," "Fortified," "Integrated," and "Revamped." - Key technologies and frameworks (Python, AWS, Azure, Docker, Pytorch, React, Rust, CUDA) are embedded directly within the descriptions of the accomplishments, showing practical application of their skills. Clear Progression Across Experiences: - The resume illustrates a clear and rapid growth trajectory, starting with an infrastructure-focused internship (AWS Cloud Intern) and progressing through machine learning, open-source development, and coaching. - The most recent roles at Elevance Health and Cognizant show a move into more complex AI and backend engineering responsibilities, demonstrating an ability to quickly learn and take on advanced tasks. I've been lucky enough to have mentors who have shared their resumes with me and I want to do the same for others. Find what VERIFIED resumes landed people interviews at Google, Meta, Microsoft: https://bit.ly/3HKbsOO Not every resume should look like this. I’m sharing it because this is what’s actually working in today’s job market. For me, I never had anyone share their resumes that got interviews at companies. It was always a black box. And if this post helps even one person get a foot in the door, then I’ll keep sharing.

The recent news on AWS center in the Middle East going down because of the war made me relive my experience decades ago! I once helped build what we proudly called a best-in-class disaster recovery architecture. We did everything right—on paper. ✔️ Business Impact Analysis done ✔️ RTO & RPO agreed with stakeholders ✔️ Sophisticated tools deployed ✔️ DR site fully provisioned We were confident. Almost too confident and then came the day that tested everything ! A dual power supply failure hit our primary data center. Within minutes, 300+ servers went down abruptly. What followed was worse than downtime: Critical application databases got corrupted AND THEN The DR site also got corrupted ! Real-time transactions came to a complete standstill. With every passing hour, we lost millions of dollars in revenue. In that moment, all our architecture diagrams, tools, and planning meant one thing: NOTHING —because the system didn’t recover !!! What this experience taught me: 1) Testing isn’t real until it’s brutal Table-top simulations give comfort. Full-scale failover drills expose truth. Test like it’s already failing: -Simulate real load -Introduce chaos scenarios -Assume components will fail unexpectedly 2) DR is not a technology problem—it’s a systems problem We focused heavily on tools. We underestimated dependencies. Ensure: -End-to-end recovery (infra + app + data integrity) -Isolation between primary and DR (to avoid cascade failures) -Backup validation, not just backup completion 3) Communication is your real recovery engine In crisis, confusion spreads faster than outages. Build: -Clear SOPs for business continuity -Pre-defined escalation paths -Regular cross-team drills (not just IT—include business teams) 4) Leadership presence changes outcomes War rooms are intense. Fatigue, panic, and noise creep in. As a tech leader: -Your presence brings calm -Your clarity drives prioritization -Your energy keeps teams going Sometimes, leadership is less about answers… and more about Stability 5) Assume your DR will fail—and design for that This was the hardest lesson. Build layers: - Immutable backups - Offline recovery options -“Last resort” recovery playbooks Because resilience is not about one backup plan. It’s about what happens when that backup plan fails... Have you ever seen a #DR plan fail in real life? How often do you run full-scale disaster recovery drills? What’s the one thing most organizations still get wrong about resilience? Curious to hear real experiences—those are always more valuable than frameworks. #DR #disasterrecovery #drill #test #BCP #leadership #technology #resilience