Cloud Computing Solutions

McKinsey & Company: "𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗿𝗲𝗾𝘂𝗶𝗿𝗲𝘀 𝗱𝗲𝗲𝗽 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝘁𝗼 𝘁𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗦𝘁𝗮𝗰𝗸". ⬇️ In its latest analysis, McKinsey illustrates how Generative AI, when properly integrated, can transform customer journeys — using the example of a travel agent bot (via AI Agent). A great example that proves: To succeed with GenAI, it's not enough to simply add a model. You have to rethink your entire system — end to end. 𝗛𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀: 𝗠𝘂𝗹𝘁𝗶-𝗹𝗮𝘆𝗲𝗿𝗲𝗱 𝗚𝗲𝗻𝗔𝗜 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻⬇️ 𝟭. 𝗖𝘂𝘁𝗼𝗺𝗲𝗿 𝗟𝗮𝘆𝗲𝗿: → The user logs in, reviews options, and either completes the task or escalates to a live agent — all without needing to understand what’s happening behind the scenes. This is the experience layer where trust, speed, and personalization matter most. 𝟮. 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿 → Manages the dialogue with the user: - Chatbot initiates and guides the conversation - Agent escalation is triggered when AI alone can’t resolve the issue 𝟯. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗟𝗮𝘆𝗲𝗿: → Executes intelligent model actions based on context: - Pulls user data - Checks policies - Generates options - Executes next steps 𝟰. 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗔𝗽𝗽 𝗟𝗮𝘆𝗲𝗿 → Connects AI to core enterprise systems: - Authentication and identity services - Policy enforcement and booking workflows - Agent assignment logic 𝟱. 𝗗𝗮𝘁𝗮 𝗟𝗮𝘆𝗲𝗿 → Provides real-time contextual inputs: - Customer ID - Booking history - Policy rules - Agent directories 𝟲. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗟𝗮𝘆𝗲𝗿 → Powers scale, performance, and governance: - Cloud or hybrid infrastructure - Model orchestration - Low-latency interaction support - Security and data governance 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Enterprises won’t win with GenAI by treating it as a bolt-on feature. The real differentiators will be those who embed AI at every layer — from user interfaces to business logic, data pipelines, and infrastructure. AI integration is not a side project. It’s a re-architecture of the digital enterprise. The unlock isn’t more models. It’s deeper integration. Full study in the comments. 𝗜 𝗲𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀 𝗮𝗿𝗼𝘂𝗻𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 — 𝗮𝗻𝗱 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗺𝗲𝗮𝗻 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 — 𝗶𝗻 𝗺𝘆 𝘄𝗲𝗲𝗸𝗹𝘆 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲 𝗳𝗼𝗿 𝗳𝗿𝗲𝗲: https://lnkd.in/dbf74Y9E

A year has passed since I last visualized the cloud provider landscape, and the changes are striking.  While each provider's strengths remain consistent, several key trends have reshaped the ecosystem: • 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗖𝗹𝗼𝘂𝗱 𝗣𝗮𝗿𝗮𝗱𝗶𝗴𝗺:  Organizations are increasingly moving away from single-provider reliance, adopting multi-cloud strategies to optimize spending, avoid vendor lock-in, and leverage best-in-breed services from various platforms. • 𝗚𝗿𝗲𝗲𝗻 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲𝘀:  Sustainability is no longer optional.  Major cloud providers are doubling down on renewable energy and providing tools for customers to monitor and reduce their environmental impact. • 𝗔𝗜/𝗠𝗟 𝗗𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻:  The accessibility of artificial intelligence and machine learning has exploded.  Providers are offering increasingly user-friendly tools, empowering businesses of all sizes to harness the power of AI. • 𝗘𝗱𝗴𝗲 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴'𝘀 𝗥𝗶𝘀𝗲:  Edge computing is transforming industries. Platforms like Azure Arc, AWS Outposts, and Google Anthos are evolving rapidly, enabling innovation in areas like IoT and real-time data processing. • 𝗦𝗲𝗿𝘃𝗲𝗿𝗹𝗲𝘀𝘀 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻: Serverless computing continues its ascent, abstracting away infrastructure complexities and allowing developers to focus on code.  Recent advancements have focused on improved tooling and broader functionality. • 𝗧𝗵𝗲 𝗥𝗲𝗽𝗮𝘁𝗿𝗶𝗮𝘁𝗶𝗼𝗻 𝗧𝗿𝗲𝗻𝗱: Interestingly, alongside cloud adoption, some companies are also exploring "reverse cloud," moving certain workloads back on-premise.  This often reflects a focus on cost optimization for specific applications or data governance requirements. The ideal cloud solution remains dependent on individual business requirements.  Regularly evaluating your cloud strategy is essential to ensure it aligns with your evolving needs. What significant shifts have you noticed in the cloud landscape lately? I'm interested in hearing your insights.

One thing is clear in Accenture’s latest report on building an AI‑ready cloud foundation; organizations aren’t just modernizing their tech stacks; they’re redefining how they create value.     What we’re seeing now is a shift from cloud as an efficiency play to cloud as the backbone of continuous reinvention. AI is accelerating that shift, but AI can only deliver its full potential when the underlying architecture is ready for it.      The companies pulling ahead are the ones treating cloud, data, and AI as one integrated system, not separate investments. They’re simplifying core operations, creating flexible digital foundations, and empowering their people with the skills and tools to move with speed and confidence.     This isn’t about chasing every new technology. It’s about building the resilience and adaptability to keep reinventing, again and again as the environment changes. At its core, an AI‑ready cloud foundation is about preparing the enterprise for what’s next, not just optimizing for today. The leaders who understand this will set the pace for their industries.    https://lnkd.in/gvrX4rqp    Andy Tay, Lan Guan, Jason Dess Jefferson Wang, Shalabh Kumar Singh 

Excited to share insights from our latest Next at Chase blog post by Praveen Tandra and Sudhir Rao where we dive into the transformative journey of migrating our data ecosystem from Hadoop to AWS. This shift is a game-changer for our data strategy, addressing tech debt and setting the stage for future innovation. Key insights from our journey: • Migration Milestone: We're moving our Data Lake from on-premises Hadoop to AWS, embracing a flexible and future-proof cloud solution. • Tackling Tech Debt: Addressing challenges like data duplication, metadata drift, and platform incompatibilities to streamline our data processes. • Adopting Open Standards: Transitioning to Apache Parquet for efficient, open-format data storage, enhancing interoperability and performance. • Project Metafix: A collaborative effort to reconcile and adapt decades-old metadata, ensuring seamless migration and data integrity. • Lineage 2.0: Mapping data movement end-to-end, providing a clear view of data assets across legacy and target platforms. None of this may be groundbreaking, but for a 225-year-old company, this migration is more than just a tech upgrade—it's a strategic leap forward in how we manage and utilize petabytes of data at Chase. Stay tuned for part two, as we continue to share our journey and the innovations driving our data transformation. So proud of all of our teams driving this forward, boom! Question for You: How do you see cloud migration impacting the future of data management? Share your thoughts below! #DataTransformation #Innovation

With the current impact of cell network outages across almost all carriers in the US, it's a good time to talk about the future; actually, it's not even about the future, it's the present. Several years ago I started talking about having mobile robotics (air, ground and maritime robotics, like drones, rovers and submergible devices) be part of a mobile adhoc network or MANET. One example is a private mesh network, like Silvus Technologies provides. These communications solutions for high bandwidth video, C2, health and telemetry data are absolutely needed in today's environment and allow for a very flexible set-up and coverage; from a local incident scene, to a much larger area coverage, to entire cities or counties being covered. Why the need? While we in the drone industry originally focused on getting drones connected to a cell network, we quickly realized the single point of failure; the cell network infrastructure. Natural disasters, as well as manmade disasters, can impact these networks dramatically. An earthquake, hurricane, a solar storm, or a cyberattack, can take down these public networks for hours to days. And that includes public safety dedicated solutions like FirstNet or Frontline, during times when coms and data push is absolutely needed. Over the past couple of years we have seen the rise of mobile robotics deployments within private networks. While the defense side has done this approach for years, the public safety sector is still new to this concept. Some solutions integrate with a variety of antennas, amplifiers and ground stations, offer low latency, high data rates (up to 100+Mpbs), 256-bit AES encryptions and allow for a very flexible and scalable mobile ad-hoc mesh network solution. And most importantly - independence from a public network system. And now imagine you have multiple devices operating; a helicopter, a drone, a ground robotic, together with individuals on the ground, all connected and all tied into a geospatial information platform, like ATAK/TAK. Each connected device can become a node and extend the range. This is what I am calling building the Tech/Tac Bubble. This is not just the future, this is already happening with a handful of agencies across the US It's time to start thinking about alternative communication solutions and mobile robotics are an important part of leading the way. #UAV #UAS #UGV #Drones #network #MANET #Meshnetwork #publicsafety

🚀 New blog post: Azure Kubernetes Application Network is now in preview, and it's worth paying attention to. If you're running multiple AKS clusters and dealing with the cross-cluster networking headache - service discovery, mTLS across boundaries, traffic shifting between versions in different clusters - this is Microsoft's answer to that problem. It's a fully managed, ambient-mode service mesh (no sidecars!) that spans multiple AKS clusters with a shared trust boundary and a single control plane you don't have to operate. In my latest post I cover: ✅ How the architecture actually works (management, control, and data plane) ✅ Step-by-step setup with real commands ✅ Making services global across clusters ✅ Traffic shifting, L4/L7 auth policies, and JWT-based routing ✅ What to watch out for before you dive in It's preview, so not for production yet - but it's worth standing up in dev now if multi-cluster AKS is on your roadmap. 👉 https://lnkd.in/e63CJdKG #Azure #AKS #Kubernetes #ServiceMesh #Istio #CloudNative #DevOps

Cloud AI Architecture This week I’ve been sharing insights on various aspects of AI governance, and today I want to dive deep into one key component - cloud based AI architecture. This example is designed to serve as a guide for any Data/AI leader looking to progress towards responsible AI development and robust governance.   The architecture should be built on layered principles that integrate both global and local regulatory requirements. Here’s a snapshot of what it covers:   Data Ingestion & Quality - Securely collect, cleanse, and store data with built in quality checks and compliance controls to ensure you always have reliable regulated data as the foundation.   Secure API & Service Integration - Expose AI models through secure APIs by leveraging encryption, robust authentication (OAuth, mutual TLS) and proper rate limiting protecting your models against unauthorized access.   Model Training & Deployment - Use containerized environments and automated CI/CD pipelines for scalable and secure model development. Ensure every change is traceable and reversible while continuously monitoring for bias and performance.   Monitoring, Governance & Human Oversight - Implement real time dashboards and detailed audit logs for continuous risk management. Integrate human in the loop controls for critical decision points to ensure that AI augments human intelligence rather than replacing it.   Cloud Security & Compliance - Design your infrastructure with stringent network security, dedicated VPCs, and adherence to data residency regulations. Secure your architecture with encryption, key management, and proactive monitoring.   This layered approach not only mitigates risks like adversarial attacks and data breaches but also supports rapid innovation. It’s a practical scalable blueprint that any organization can adopt to build a secure responsible AI ecosystem.   Want to advance your AI approach? Let's connect and explore possibilities.

Why the EU’s Cloud Moves Signal a Global Cloud Evolution We're watching a pivotal conversation unfold in the European Union right now—one that is already shaking up how enterprises think about their cloud strategies worldwide. For years, the major US-based public cloud hyperscalers—AWS, Azure, and Google Cloud—have dominated the market, driving innovation but also concentrating workloads and, frankly, risk. But the EU is taking a second look. In the wake of high-profile outages and mounting costs, policymakers and enterprises are openly questioning the wisdom of relying so heavily on a handful of hyperscalers based outside their borders. We’re now seeing a push toward distributing workloads to a wider array of providers: sovereign clouds, colocation partners, regional managed service providers, and other non-US cloud entities. Why does this matter beyond Europe? Because these decisions set precedents that the rest of the world is sure to study carefully. If the EU successfully reduces its dependency on US hyperscalers, I believe other regions—facing similar concerns over data sovereignty, resiliency, and cost—will follow suit in the next 2–3 years. Many enterprises may not talk about it publicly, but I’m convinced that the “great cloud reevaluation” is quietly underway across the globe. Does this mean the major hyperscalers will lose significant revenue? Honestly, I doubt it. The exponential growth of AI and generative AI workloads will more than fill any gaps left by traditional enterprise migrations. But what we will see—and should encourage—is a safer, more resilient, and more distributed cloud landscape. Our critical workloads and data sets belong across a diverse mix of platforms: on-premises, across multiple clouds, and yes, with trusted regional partners. I’ll be following this story closely. I recommend you do too. The next phase of cloud is about optionality, resilience, and smart risk management—and the EU’s decisions are a key signpost for what comes next. #cloud #multicloud #digitaltransformation #cloudstrategy #EU #cloudcomputing #datasecurity

Many smart people treat cloud migration as merely an IT or engineering task. This narrow view often leads to failures. What’s missing is the focus on outcomes rather than just tasks ⬇️ Cloud migration isn't just about transferring data; it involves higher costs but offers greater benefits like improved product usage tracking and seamless updates. The key is understanding these benefits and aligning them with business goals. As a product manager, your role is to steer the migration process by identifying which components are crucial for users and require modernization. It's not a one-time task. Begin with critical parts that need updates, and rethink your user experience—don’t just replicate the old mainframe architecture. This is your chance to enhance user experience, gain better data insights, and increase business value. The goal isn’t just moving to the cloud but transforming how your product meets customer needs. Recognize the strategic value of cloud migration. Ensure everyone understands why it’s necessary and highlight the potential benefits. Approach migration piece by piece, evaluating where you can deliver customer value immediately. Remember, mishandling the transition can lead to churn. It's about managing risks, maintaining a clear vision, and understanding the long-term benefits of cloud adoption. Use this opportunity not just to migrate but to innovate and improve customer satisfaction. If you'd like more insights or have questions, comment below or drop your question on the Dear Melissa website!

Imagine you have 5 TB of data stored in Azure Data Lake Storage Gen2 — this data includes 500 million records and 100 columns, stored in a CSV format. Now, your business use case is simple: ✅ Fetch data for 1 specific city out of 100 cities ✅ Retrieve only 10 columns out of the 100 Assuming data is evenly distributed, that means: 📉 You only need 1% of the rows and 10% of the columns, 📦 Which is ~0.1% of the entire dataset, or roughly 5 GB. Now let’s run a query using Azure Synapse Analytics - Serverless SQL Pool. 🧨 Worst Case: If you're querying the raw CSV file without compression or partitioning, Synapse will scan the entire 5 TB. 💸 The cost is $5 per TB scanned, so you pay $25 for this query. That’s expensive for such a small slice of data! 🔧 Now, let’s optimize: ✅ Convert the data into Parquet format – a columnar storage file type 📉 This reduces your storage size to ~2 TB (or even less with Snappy compression) ✅ Partition the data by city, so that each city has its own folder Now when you run the query: You're only scanning 1 partition (1 city) → ~20 GB You only need 10 columns out of 100 → 10% of 20 GB = 2 GB 💰 Query cost? Just $0.01 💡 What did we apply? Column Pruning by using Parquet Row Pruning via Partitioning Compression to save storage and scan cost That’s 2500x cheaper than the original query! 👉 This is how knowing the internals of Azure’s big data services can drastically reduce cost and improve performance. #Azure #DataLake #AzureSynapse #BigData #DataEngineering #CloudOptimization #Parquet #Partitioning #CostSaving #ServerlessSQL