Cloud Computing Solutions

Demystifying Cloud Strategies: Public, Private, Hybrid, and Multi-Cloud As cloud adoption accelerates, understanding the core cloud computing models is key for technology professionals. In this post, I'll explain the major approaches and examples of how organizations leverage them. ☁️ Public Cloud Services are hosted on shared infrastructure by providers like AWS, Azure, GCP. Scalable, pay-as-you-go pricing. Examples: - AWS EC2 for scalable virtual servers   - S3 for cloud object storage - Azure Cognitive Services for AI capabilities - GCP Bigtable for large-scale NoSQL databases ☁️ Private Cloud Private cloud refers to dedicated infrastructure for a single organization, enabling increased customization and control. Examples:  - On-prem VMware private cloud - Internal Openstack private architecture - Managed private platforms like Azure Stack - Banks running private clouds for security ☁️ Hybrid Cloud Hybrid combines private cloud and public cloud. Sensitive data stays on-prem while leveraging public cloud benefits. Examples: - Storage on AWS S3, rest of app on-prem - Bursting to AWS for seasonal capacity - Data lakes on Azure with internal analytics ☁️ Multi-Cloud Multi-cloud utilizes multiple public clouds to mitigate vendor lock-in risks. Examples:  - Microservices across AWS and Azure  - Backup and DR across AWS, Azure, GCP - Media encoding on GCP, web app on Azure ☁️ Hybrid Multi-Cloud The emerging model - combining private infrastructure with multiple public clouds for ultimate flexibility. Examples: - Core private, additional capabilities leveraged from multiple public clouds - Compliance data kept private, rest in AWS and Azure  - VMware private cloud extended via AWS Outposts and Azure Stack Let me know if you have any other questions!

One of the most important laws of frugal architecture is that you can’t optimize what you can’t measure. I learned this long before cloud computing. Growing up in Amsterdam during the energy crisis of the 1970s, we had things like car-free Sundays and rationed energy, but the detail that always stuck with me was closer to home. Households with their energy meter on the main floor of their homes used significantly less energy than those with it hidden in the basement. The same style of house, in the same city, yet dramatically different behaviour. About as clear of a signal as you can get that seeing data changes what you do with it. For years, in the absence of better sustainability metrics, usage (or consumption) was the best proxy we had. The meter was in the basement. With the AWS Sustainability Console, we bring the meter to your “living room”. It gives your builders direct access to Scope 1, 2, and 3 emissions data, broken down by service and Region, exportable via API, without ever touching sensitive cost and billing data. The right data, to the right people, through the right door. When carbon emission becomes just another metric in your observability stack sitting next to latency, cost, and error rates, it stops being a compliance exercise and starts becoming an architectural discipline. The world we are building in the cloud is the world we are leaving to our children. Measure it like it matters. Read more here: https://lnkd.in/efFjU7hG

📌Turning Waste into Warmth: A Smarter Way Forward 🔁🔥 Finland is transforming how cities use energy by integrating sustainability directly into digital infrastructure. New underground data centers in Helsinki are designed not only to host servers but also to recycle the immense heat they generate. Instead of venting this waste energy, it’s captured and redirected into district heating systems that warm nearby homes and buildings. This closed-loop approach allows the same energy that powers cloud computing to heat thousands of apartments, reducing reliance on fossil fuels and cutting urban carbon emissions dramatically. Data centers, once known for their high energy consumption, are becoming key players in renewable urban ecosystems. This is the kind of circular solution modern facilities must aspire to. By integrating technology, engineering, and smart planning, even high-energy systems like data centres can become contributors to a greener city. For facilities and estates professionals, the message is clear: Sustainability isn’t always about new resources — it’s about using what we already have, better. The project underscores Finland’s leadership in green innovation — turning what was once environmental waste into community benefit. As cities worldwide search for climate solutions, this model shows how technology and sustainability can work hand in hand to reshape the future of energy. A powerful reminder of what’s possible when we rethink infrastructure with efficiency and environmental responsibility at the core. Sources: ✍️TechTimes #GreenEnergy #FinlandInnovation #SustainableCities #DataCenters #CleanTechnology #Infrastructure #Environmental #Technology

I reduced our Annual AWS bill from ₹15 Lakhs to ₹4 Lakhs — in just 6 months. Back in October 2024, I joined the company with zero prior industry experience in DevOps or Cloud. The previous engineer had 7+ years under their belt. Just two weeks in, I became solely responsible for our entire AWS infrastructure. Fast forward to May 2025, and here’s what changed: ✅ ECS costs down from $617 to $217/month — 🔻64.8% ✅ RDS costs down from $240 to $43/month — 🔻82.1% ✅ EC2 costs down from $182 to $78/month — 🔻57.1% ✅ VPC costs down from $121 to $24/month — 🔻80.2% 💰 Total annual savings: ₹10+ Lakhs If you’re working in a startup (or honestly, any company) that’s using AWS without tight cost controls, there’s a high chance you’re leaving thousands of dollars on the table. I broke everything down in this article — how I ran load tests, migrated databases, re-architected the VPC, cleaned up zombie infrastructure, and built a culture of cost-awareness. 🔗 Read the full article here: https://lnkd.in/g99gnPG6 Feel free to reach out if you want to chat about AWS, DevOps, or cost optimization strategies! #AWS #DevOps #CloudComputing #CostOptimization #Startups

4 strategies for multi-GPU training explained visually. By default, deep learning models only utilize a single GPU for training, even if multiple GPUs are available. An ideal way to proceed (especially in big-data settings) is to distribute the training workload across multiple GPUs. The graphic below depicts four common strategies for multi-GPU training: 1) Model parallelism - Different parts (or layers) of the model are placed on different GPUs. - Useful for huge models that do not fit on a single GPU. - However, model parallelism also introduces severe bottlenecks as it requires data flow between GPUs when activations from one GPU are transferred to another GPU. 2) Tensor parallelism - Distributes and processes individual tensor operations across multiple devices or processors. - It is based on the idea that a large tensor operation, such as matrix multiplication, can be divided into smaller tensor operations, and each smaller operation can be executed on a separate device or processor. - Such parallelization strategies are inherently built into standard implementations of PyTorch and other deep learning frameworks, but they become much more pronounced in a distributed setting. 3) Data parallelism - Replicate the model across all GPUs. - Divide the available data into smaller batches, and each batch is processed by a separate GPU. - The updates (or gradients) from each GPU are then aggregated and used to update the model parameters on every GPU. 4) Pipeline parallelism - This is often considered a combination of data parallelism and model parallelism. - So the issue with standard model parallelism is that 1st GPU remains idle when data is being propagated through layers available in 2nd GPU: - Pipeline parallelism addresses this by loading the next micro-batch of data once the 1st GPU has finished the computations on the 1st micro-batch and transferred activations to layers available in the 2nd GPU. - The process looks like this: ↳ 1st micro-batch passes through the layers on 1st GPU. ↳ 2nd GPU receives activations on 1st micro-batch from 1st GPU. ↳ While the 2nd GPU passes the data through the layers, another micro-batch is loaded on the 1st GPU. ↳ And the process continues. - GPU utilization drastically improves this way. This is evident from the animation below where multi-GPUs are being utilized at the same timestamp (look at t=1, t=2, t=5, and t=6). -- If you want to learn AI/ML engineering, I have put together a free PDF (530+ pages) with 150+ core DS/ML lessons. Get here: https://lnkd.in/gi6xKmDc -- 👉 Over to you: What are some other strategies for multi-GPU training?

It's astonishing that $180 billion of the nearly $600 billion on cloud spend globally is entirely unnecessary. For companies to save millions, they need to focus on these 3 principles — visibility, accountability, and automation. 1) Visibility The very characteristics that make the cloud so convenient also make it difficult to track and control how much teams and individuals spend on cloud resources. Most companies still struggle to keep budgets aligned. The good news is that a new generation of tools can provide transparency. For example: resource tagging to automatically track which teams use cloud resources to measure costs and identify excess capacity accurately. 2) Accountability Companies wouldn't dare deploy a payroll budget without an administrator to optimize spend carefully. Yet, when it comes to cloud costs, there's often no one at the helm. Enter the emerging disciplines of FinOps or cloud operations. These dedicated teams can take responsibility of everything from setting cloud budgets and negotiating favorable controls to putting engineering discipline in place to control costs. 3) Automation Even with a dedicated team monitoring cloud use and need, automation is the only way to keep up with the complex and evolving scenarios. Much of today's cloud cost management remains bespoke and manual, In many cases, a monthly report or round-up of cloud waste is the only maintenance done — and highly paid engineers are expected to manually remove abandoned projects and initiatives to free up space. It’s the equivalent of asking someone to delete extra photos from their iPhone each month to free up extra storage. That’s why AI and automation are critical to identify cloud waste and eliminate it. For example: tools like "intelligent auto-stopping" allow users to stop their cloud instances when not in use, much like motion sensors can turn off a light switch at the end of the workday. As cloud management evolves, companies are discovering ways to save millions, if not hundreds of millions — and these 3 principles are key to getting cloud costs under control.

There you have it. On March 1, 2026, Iranian Shahed drones struck two Amazon AWS data centers in the UAE and damaged a third in Bahrain. The first deliberate state attack on commercial cloud infrastructure in history. This isn't just an Amazon problem. It's a DoW problem. We spent decades building GovCloud — a handful of hyper-classified, physically concentrated regions with brutal security requirements, limited vendors, and massive bottlenecks. The promise was security through isolation. What we actually built was a fragile, concentrated, high-value target list. AWS has 39 geographic regions. The unclassified US GovCloud has two. You know what Iran knows? Exactly where they are. I've been saying this for years at the DoD: concentration is not security. Geographic distribution is resilience. The commercial cloud, properly secured with the right Zero Trust architecture and a real security stack, gives you hundreds of availability zones across dozens of countries. An adversary would need to simultaneously destroy a global distributed network to meaningfully degrade operations. That's a fundamentally harder problem than taking out a building in Northern Virginia. GovCloud made sense in 2010 when "cloud" was still a dirty word in uniform and the threat model was insider leakage. The threat model in 2026 is different. Peer adversaries with precision strike capability targeting the exact nodes they believe power our AI-assisted operations. The fix isn't to abandon classified environments. It's to distribute them. Use commercial multi-cloud architectures — AWS, Azure, Google — with proper security stacks, Zero Trust, cryptographic isolation, and geographic redundancy baked in. JWCC was a step in that direction. We need to move faster. Because right now, if someone takes out the right two buildings, US military AI goes dark. That's not resilience. That's a target.

The cloud divorce is happening. And most organisations aren't ready for either side of it. Three weeks ago at Mobile World Congress, the European Commission launched EURO-3C. A €75 million project to build Europe's first federated edge-cloud infrastructure. 70+ organisations across 13 countries. Not because they love spending money. Because they've realised their data lives in someone else's country, under someone else's laws, and they can't guarantee where it goes once it leaves the device. Meanwhile, Azure UK South is struggling. If you've tried to deploy GPU-enabled VMs recently, you'll know. AllocationFailed. ZonalAllocationFailed. Quota requests that used to be auto-approved are now manually reviewed. Subreddits and community boards are filling up with engineers hitting the same walls. Microsoft's own Q&A forums show models being pulled from UK South entirely, with access restricted to what they're calling "strategically prioritized customers." West Europe is the same story. Microsoft's response? A new campus in North Yorkshire on the site of a decommissioned 1,960MW power station. Now being converted into compute.. They consumed so much power they need to become the power station. But, do we actually need all of this? Yes, AI workloads are genuinely demanding. That's real. But underneath the AI gold rush, everyday software has become obscenely resource-hungry. Teams & Chrome are unusable on an 8GB laptop if you want to do anything else. Windows ships with so much telemetry, spyware and background processing that a fresh install immediately starts phoning home to half the internet. Ten years ago, we ran entire businesses on a fraction of this compute. It worked & We didn't need a nuclear reactor to power the email server. We've normalised bloat. We've accepted that a video call needs 4GB of RAM. And now we're building power stations to run the cloud that runs the bloat. The repatriation numbers tell the story. 83% of enterprises plan to leave public cloud. 61% of Western European CIOs are shifting local. Sovereign cloud spending: $80 billion this year. But the generation of engineers who knew how to build efficient, lean infrastructure from scratch? We stopped training them a decade ago. You can't repatriate what you can't rebuild. And you can't rebuild efficiently if the software running on top demands ten times the resources it should. I've been watching this from both sides. I architect Azure environments during the day. At night, I run my own infrastructure. I'm migrating my email into a European data centre in Helsinki. My monitoring runs on a Raspberry Pi - hardware that costs less than a month of Teams licensing. The cloud isn't going anywhere. But the assumption that everything belongs there, that infinite scale is infinite, that someone else's data centre is always the right answer? That assumption is running out of power. Literally. www.readthemanual.co.uk #digitalsovereignty #selfhosting #homelab #azure

As a data engineer, migrating from On-prem to cloud is one of the most common use-cases. Before understanding the various factors to consider here are few common real time usecase of migration - 1. A retail company migrating its data warehouse to the cloud can leverage real-time analytics for inventory management and customer behavior analysis. 2. A healthcare organization moving patient data to a HIPAA-compliant cloud service can improve data security while enhancing accessibility for authorized personnel. 3. A financial institution transitioning to cloud-based data lakes can more easily implement fraud detection algorithms and personalized banking services. Cloud migration offers numerous benefits but also presents unique challenges that require careful planning and execution. 📍Scalability: Cloud platforms provide virtually unlimited resources, allowing data engineers to easily scale their infrastructure as data volumes grow. 📍Cost-efficiency: Pay-as-you-go models can significantly reduce capital expenditure on hardware and maintenance costs. 📍Advanced analytics capabilities: Cloud providers offer cutting-edge tools for big data processing, machine learning, and AI integration. 📍Global accessibility: Cloud-based data can be accessed from anywhere, facilitating collaboration and remote work. 📍Automated maintenance: Cloud providers handle most infrastructure maintenance, allowing data engineers to focus on data-related tasks. Here are few reference architectural visuals curated by ZingMind Technologies, Arun Kumar - Google Cloud architecture, Amazon Web Services (AWS) and Microsoft Azure. Here are some key factors for data engineers to consider: - Data security & compliance: Ensure that the chosen cloud provider meets industry-specific regulations (e.g., GDPR, CCPA). - Data volume and transfer speed: Large datasets may require physical data transfer methods like AWS Snowball or Azure Data Box. - Application dependencies: Some legacy systems may require refactoring or replacement to work efficiently in the cloud. - Skills gap: Team members may need training to work effectively with cloud technologies. - Cost management: While cloud can be cost-effective, improper resource allocation can lead to unexpected expenses. - Data governance: Implement robust policies for data access, retention, and deletion in the cloud environment. - Hybrid & multi-cloud strategies: Consider whether a hybrid approach or multi-cloud strategy best suits your organization's needs. - Performance optimization: Ensure that data access patterns are optimized for cloud architecture to maintain or improve performance. - Disaster recovery & business continuity: Leverage cloud provider's tools for backup and failover mechanisms. - Vendor lock-in: Be aware of potential difficulties in migrating between cloud providers in the future. #cloud #data #engineering

Over the last 7 years, IBM has been quietly building something quite deliberate. Not a single product. Not a one off platform. But a set of capabilities that, taken together, form the operating backbone for enterprise AI. You can see the pattern when you step back: Foundation: Red Hat Performance: IBM Instana and IBM Turbonomic Governance: Apptio, an IBM Company and HashiCorp Integration and data: Webmethods and DataStax Flow: now strengthened with Confluent Individually, each of these solves a specific problem. Together, they start to look more like a system. For telecom operators, that matters. Telcos are not short of data. They are not short of platforms. What they are often dealing with is fragmentation, latency between systems, and the challenge of turning insight into action at scale. AI only works in that environment if a few things are true: Data moves in real time Systems are observable Resources are optimised continuously Governance is built in, not bolted on That is where this kind of architecture becomes relevant. Not as a “data fabric” concept, but as a way of running complex, distributed environments where decisions need to be made inside the operational loop, not after the fact. In telecoms, that translates into very practical outcomes: Better network performance Faster issue resolution More efficient use of infrastructure Lower cost to serve The interesting question now is not whether the components exist. It’s whether operators bring them together in a way that actually changes how their business runs. Because in telecoms, AI will be judged by how deeply it is embedded into the operating model and how it influences performance, efficiency and outcomes over time, not how impressive it looks in isolation. #Telecoms #AI #DataStreaming #Observability #FinOps #Cloud #TelcoTransformation Alison Clegg James Stewart Kash Hussain Callum Simpson Alexander Verdi Elke Kunde Begüm Daşkaya Gökhan Yılmaz Chantelle Govender Titus Masike