How AI is Transforming Data Science: 5 Trends for 2025

Most digital twin projects fail. Not from lack of data, but from bad data modeling. Traditional approaches rely on rigid schemas, ex-model measures, and endless JOINs. The result: long implementation times, high maintenance costs, and models that can’t keep up with changing business needs. 🔑 A better foundation: Ontology modeling in SQL Here’s why it changes the game: ✅ Unified semantics across diverse data sources ✅ Query across IoT streams, legacy systems, and cloud platforms without transformation ✅ Relationships as first-class citizens, not hidden in JOIN logic ✅ Efficient measures in SQL ✅ Reusable, modular models that scale ✅ A semantic layer that AI agents can reason over With SQL ontologies, digital twins are faster to implement, easier to maintain, and AI-ready from day one. 👉 Read the full blog here: https://lnkd.in/deUHNAn2 #DigitalTwins #AI #SQL #KnowledgeGraphs #SemanticLayer #Ontology

Your teams are collecting more data than ever. But are they making better, faster and timely decisions? Data without a framework for action is just noise. This is why top-performing COOs are shifting their focus from data collection to "Augmented AI with engineering." It’s not about replacing your best people. It's about giving them superpowers. Augmented AI with engineering is a system that turns real-time operational data into a competitive advantage by: Connecting the Field to the Office (IoT): Moving beyond dashboards to a live, continuous stream of high-quality data from your operations. Simulating the Future (Digital Twin): Transforming live data for running real-time simulations, raising exceptions to your team to predict failures before they happen, not just analyze them after. Automating Wisdom (AI/ML): Embedding your automated supervisory systems that work 24/7 with your best engineers' decision-making logic contextualization, ensuring consistency and safety at scale. The result isn't just efficiency. It's a fundamental shift from reactive problem-solving to proactive performance optimization. You stop asking "What happened?" and start asking "What's the best thing that could happen next?" What's the biggest barrier to this in most organizations: technology, process, or people? #Leadership #Innovation #ArtificialIntelligence #AI #Operations #DigitalTransformation ♻️ Valuable? Repost to share value with someone in your network. 🛎️ Follow me, https://lnkd.in/g4BbJvZi, for more on digital transformation and AI.

🚀 AI automation trends in 2025 are not just buzz—they’re reshaping industries with hyperautomation and AI agents that learn and adapt! ✨ Here’s why this matters: • AI-driven automation could add $13 trillion to the global economy by 2025 (McKinsey) 🌍 • Hyperautomation blends AI, RPA & machine learning to automate complex workflows end-to-end • Predictive analytics & IoT integration enable smarter, real-time decision-making • AI agents are evolving into autonomous collaborators, handling multi-step tasks across sectors like finance, healthcare, and logistics Imagine AI not just doing tasks but thinking and adapting alongside your team. The future is about humans + AI agents working side-by-side to unlock unprecedented productivity and innovation. How is AI automation transforming your business? What’s your biggest challenge or win with AI agents so far? Share below! 👇 What are your thoughts? Share in the comments! 💬 #AI #Innovation #AIAutomation #DigitalTransformation #EnterpriseAI

Which AI trends are driving change in manufacturing in 2025? Learn more in this week's virtual conference: https://lnkd.in/dPJBDibF click subscribe! According to IoT Analytics new Industrial AI Market Report 2025–2030, the market reached $43.6B in 2024 and is forecasted to grow 3.5x by 2030, fueled by developments in GenAI, edge computing, and scalable data infrastructure. Here are 8 key insights from the IoT Analytics research: ✔ Industrial AI spending remains low but rising quickly: US manufacturers invest just 0.1% of revenue into AI, but large firms are now scaling ✔ Most large manufacturers now have CEO-led AI strategies – a very different picture to what we observed when we last looked at our 2021 Industrial AI Market Report. ✔ Quality & inspection leads as the top use case, with vision systems seeing the strongest ROI ✔ Scalable data architectures and DataOps are becoming strategic imperatives ✔ Training and upskilling are emerging as critical AI enablers across operations and IT ✔ Industrial copilots (=GenAI assistants embedded in manufacturing software) are becoming standard features ✔ Edge AI is gaining traction due to latency and cost concerns, powered by new chipsets and orchestration platforms ✔ Domain-specific foundation models are emerging to “speak the language of engineering” Read more: https://lnkd.in/eBSy7jvw ⁉What Industrial AI topics are currently top of mind for you or your organization? ✅ Subscribe to #global5gevolution newsletter https://lnkd.in/ge9gsyjE ✅ Or subscribe #global5gevolution YouTube https://lnkd.in/g8M7YvKq ✅ Follow us Kaneshwaran Govindasamy & Global 5G Evolution 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝘁𝗵𝗲 𝗚𝗹𝗼𝗯𝗮𝗹 𝟱𝗚 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆! Your support helps us continue delivering the latest insights, research & conference discussions. Every contribution enables us to sustain & grow this platform for the benefit of all members 𝗖𝗵𝗼𝗼𝘀𝗲 𝘆𝗼𝘂𝗿 𝘄𝗮𝘆 𝘁𝗼 𝘀𝘂𝗽𝗽𝗼𝗿𝘁: 👉Small monthly recurring donation: https://lnkd.in/e4MAD7pN 👉One-time donation: https://lnkd.in/eitCeewX #IndustrialAI

🚀 Embracing the Power of #DigitalTwin & #AI in 2025! 🌐✨ 🎯🎯🎯🎯 At the recent Future Digital Twin & AI USA 2025 event in Houston 🇺🇸, industry leaders spotlighted how Digital Twins are transforming business intelligence from static dashboards to dynamic decision-making engines that interact in real-time with people, processes, and technology. 🤖💡 🎯🎯🎯 Imagine clicking a button and instantly influencing IoT devices out in the field based on AI-enhanced digital twin insights — that’s not the future, it’s happening now! 💥 The magic lies in combining edge computing, cloud-enabled hardware, and software to create virtual replicas that update in real-time. This reduces latency, boosts responsiveness, and scales predictive capabilities like never before. 🌩️⚡ 🎯🎯 🌱 From manufacturing shop floors optimizing operations, to agricultural digital twins revolutionizing crop management (92% accuracy in simulated environments! 🍓), to aerospace manufacturing augmenting human inspectors — the possibilities are endless! ✈️🏭 🎯 🔗 Yet, less than expected are fully leveraging this synergy between AI and digital twins. If you’re not integrating AI to unlock the full power of your digital twin strategy, there’s a huge gap in competitive advantage waiting to be filled. 📊🚀 💡 As generative AI reshapes USD standards, the edge-enabled digital twin ecosystem is poised to go out of this world. 🌍🌌 Questions on how to future-proof your enterprise with these game-changing innovations? Let’s connect! 🤝🔍 #FutureTech #Industry40 #EdgeComputing #AIInnovation #DigitalTransformation #IoT

We’ve seen this movie before 🍿: the internet left our desks and moved into our pockets -- and everything changed. The next shift is here: AI agents are moving from the cloud to the edge ☁️➡️📲. Why now? Small Language Models (SLMs) plus techniques like quantization, pruning, and distillation make on-device inference real. The result feels less like waiting on a server and more like talking to your own JARVIS 🤖-- only private, compliant, and fast. What this unlocks: ⚡ Responsiveness that feels conversational, not like a loading bar 🔒 Trust by design: your data stays on your device or within your walls 🔋💸 Lower ongoing cost/energy and fewer trips to the cloud 🎯 Deep personalization across IoT, robotics, and autonomous systems Imagine the possibilities: 🏠 Smart homes that anticipate your needs without pinging a server. 🏭 Factory robots coordinating tasks on $99 devices. 🩺📱 Personalized health advisors running entirely on your phone. Just as the mobile revolution unlocked billions of new interactions, edge AI will spawn new products, business models, and careers. Which AI-powered experience do you wish lived on your device instead of in the cloud? 💡🤔 #AI #EdgeAI #OnDeviceAI #AgenticAI #SLM #LLM #GenAI #TinyML #LLMOps #IoT #SmartDevices #Robotics #OfflineFirst #PrivacyByDesign #DataSecurity #FederatedLearning #EmbeddedAI #MobileAI #RAG #Quantization #Distillation #Pruning #ComputeAtTheEdge #Mistral #Llama #Phi3 #FutureTech #EdgeComputing #OnPrem

⚙️ The Symbiotic Relationship: AI & Data Centers 🤝 Core Interdependence ➡️ AI Runs on Data: The precision and usefulness of AI are fueled by vast amounts of high-quality data. ⬅️ AI Optimizes Data Centers: AI itself is used to make data centers more efficient through predictive maintenance and dynamic management. 🏗️ Data Centers: The AI Backbone Modern data centers are the essential infrastructure for AI, providing: 💻 Massive Computing Power: Housing GPU-accelerated servers for real-time data processing. ⚡ High-Speed Connectivity: Advanced networking for seamless data flow between AI clusters and clouds. 🛡️ Security & Resilience: Secure, resilient systems that keep AI applications running 24/7. 🔥 Evolving for AI Demands 📶 High Density: New facilities are designed for rack densities exceeding 100kW to handle intense AI workloads. 🎯 Purpose-Built: Next-gen data centers feature architectures specifically built for AI model training and inference. 🌐 The Rise of Edge Computing The convergence of AI, 5G, and IoT is pushing compute power closer to users. Edge data centers in smaller cities are vital for ultra-low-latency services in: 🎮 Gaming 🏥 Telemedicine 🏙️ Smart Cities 🔮 Strategic Importance Data centers are now the cornerstone of learning, intelligence, and innovation. With strategic investments, India is positioned to lead the world's digital future.

𝗜𝗼𝗧 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 𝗰𝗮𝗽𝘁𝘂𝗿𝗲 𝗱𝗮𝘁𝗮. 𝗔𝗜/𝗠𝗟 𝗺𝗮𝗸𝗲𝘀 𝗶𝘁 𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁. Huge thanks to Dr. Katerina Stamou, PhD (PhD in Blood flow modelling), for an insightful workshop on “𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗻𝗴 𝗠𝗤𝗧𝗧 𝘄𝗶𝘁𝗵 𝗔𝗜/𝗠𝗟”! It gave me fresh perspectives on turning raw sensor streams into actionable intelligence, especially for 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗺𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲. 𝗠𝘆 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀:  • IoT without AI = “data exhaust.”  • AI without IoT = models sitting idle.  • Together = powerful insights and automation. 𝗨𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: 🔹 Predictive Maintenance: proactively schedule maintenance using sensor insights 🔹 Anomaly Detection: identify unexpected patterns for safety and efficiency 🔹 Smart Automation: trigger immediate actions from data One question that really got me thinking: should ML models for IoT run on the edge for speed, or in the cloud for computational power? In practice, it’s not just “edge vs cloud”—it’s about smartly partitioning the ML pipeline: ⚡ 𝗘𝗱𝗴𝗲: preprocessing, feature extraction, latency-critical inference ☁️ 𝗖𝗹𝗼𝘂𝗱: deep learning, fleet-level insights, model retraining 𝗠𝗤𝗧𝗧’𝘀 𝗿𝗼𝗹𝗲: the efficient highway carrying raw data where needed, condensed insights where possible to save bandwidth. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: In aviation predictive maintenance, should edge devices detect anomalies onboard, or should raw sensor data go to the cloud first for validation and inference? 𝘈𝘵𝘵𝘢𝘤𝘩𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘵𝘢𝘬𝘦𝘢𝘸𝘢𝘺𝘴 𝘧𝘳𝘰𝘮 𝘵𝘩𝘦 𝘸𝘰𝘳𝘬𝘴𝘩𝘰𝘱. 💡 I’d love to hear how others in AI/IoT tackle pipeline partitioning, MQTT design, and edge/cloud tradeoffs — especially in real-world predictive maintenance scenarios. #MQTT #IoT #AI #PredictiveMaintenance #MachineLearning #EdgeComputing #CloudComputing #DataScience

𝑻𝒉𝒆 𝒄𝒍𝒐𝒔𝒆𝒓 𝑨𝑰 𝒈𝒆𝒕𝒔 𝒕𝒐 𝒚𝒐𝒖, 𝒕𝒉𝒆 𝒎𝒐𝒓𝒆 𝒑𝒐𝒘𝒆𝒓𝒇𝒖𝒍 𝒊𝒕 𝒃𝒆𝒄𝒐𝒎𝒆𝒔.   𝑩𝒖𝒕 𝒘𝒉𝒆𝒓𝒆 𝒕𝒉𝒂𝒕 𝒊𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 𝒍𝒊𝒗𝒆𝒔 𝒄𝒉𝒂𝒏𝒈𝒆𝒔 𝒆𝒗𝒆𝒓𝒚𝒕𝒉𝒊𝒏𝒈. 𝐂𝐥𝐨𝐮𝐝 𝐀𝐈, 𝐄𝐝𝐠𝐞 𝐀𝐈, 𝐚𝐧𝐝 𝐎𝐧-𝐃𝐞𝐯𝐢𝐜𝐞 𝐀𝐈 aren’t just buzzwords, they define where intelligence actually happens. As AI adoption grows, we’re witnessing a massive shift in how models are deployed and optimized. Here’s the breakdown ⬇️ 📌 𝐋𝐞𝐯𝐞𝐥 1 → 𝑪𝒍𝒐𝒖𝒅 𝑨𝑰 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐞𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 → Heavy LLMs run on powerful servers, accessed via APIs. 𝐒𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐛𝐮𝐭 𝐃𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 → Great for complex workloads, but needs internet + high bandwidth. 💡 Think: Generative AI tools, enterprise-scale analytics, recommendation engines. 📌 𝐋𝐞𝐯𝐞𝐥 2 → 𝑬𝒅𝒈𝒆 𝑨𝑰 𝐋𝐨𝐜𝐚𝐥𝐢𝐳𝐞𝐝 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 → Moves computation closer to IoT devices, gateways, or vehicle computers. 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐀𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞 → Lower latency + faster responses, but limited by device capacity. 💡 Think: Autonomous vehicles, smart cities, industrial IoT systems. 📌 𝐋𝐞𝐯𝐞𝐥 3 → 𝑶𝒏-𝑫𝒆𝒗𝒊𝒄𝒆 𝑨𝑰 𝐅𝐮𝐥𝐥𝐲 𝐄𝐦𝐛𝐞𝐝𝐝𝐞𝐝 → AI runs directly on chips like neural engines or AI accelerators. 𝐏𝐫𝐢𝐯𝐚𝐭𝐞 & 𝐅𝐚𝐬𝐭 → No internet needed, optimized with lightweight / quantized models. 💡 Think: Personal assistants, wearables, privacy-first healthcare apps. 𝐓𝐡𝐞 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝑪𝒍𝒐𝒖𝒅-𝒇𝒊𝒓𝒔𝒕 → Relies on massive data centers for compute + storage. 𝑬𝒅𝒈𝒆-𝒆𝒏𝒂𝒃𝒍𝒆𝒅 → Balances cloud + local to reduce latency. 𝑫𝒆𝒗𝒊𝒄𝒆-𝒆𝒎𝒃𝒆𝒅𝒅𝒆𝒅 → Brings AI directly into your pocket. The future of AI isn’t “𝐞𝐢𝐭𝐡𝐞𝐫-𝐨𝐫.” It’s 𝐡𝐲𝐛𝐫𝐢𝐝 - using the right layer for the right job 

Is the Transformer architecture dead? 🤯 Google DeepMind just unveiled a new AI model that's 2x faster and uses half the memory. Imagine the traditional Transformer as a hospital where every patient (or "token") goes through every single department, regardless of the ailment. MoR, or Mixture-of-Recursions, is a new kind of hospital. Its lightweight "router" intelligently triages each token, sending simple ones home quickly while routing complex ones for deeper, recursive passes. Here’s why it's a paradigm shift: Smarter, Not Bigger: Reuses a single set of shared layers, dramatically cutting down on parameters. Inference Efficiency: The result is up to 2x faster inference and a 50% reduction in memory usage. Democratizing AI: This efficiency could bring more powerful AI to resource-constrained devices, from mobile phones to IoT. This isn't just an optimization; it's a fundamental rethinking of how LLMs reason and use computational resources. What are your thoughts? Is this the beginning of the post-Transformer era, or just an exciting new path forward? Share your take below! 👇 #AI #MachineLearning #DeepLearning #LLM #MoR #Transformer #GoogleDeepMind #TechInnovation