Sovit Garg’s Post

💡 Just wrapped up an intensive read of 𝑨𝑰 𝑬𝒏𝒈𝒊𝒏𝒆𝒆𝒓𝒊𝒏𝒈 by Chip Huyen, and my mind is buzzing! There’s nothing more critical to professional growth than continuous learning and research, especially in a field evolving as quickly as AI. This book provided a deep-dive into the practical, production-grade realities of building robust AI applications. The core concepts truly emphasize the shift from models to systems: 𝗣𝗿𝗼𝗺𝗽𝘁 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 & 𝗗𝗲𝗳𝗲𝗻𝘀𝗲: Learned how to craft effective, explicit prompts for desired outcomes, and critically, how to implement defenses against attacks like prompt extraction and jailbreaking. Safety and clarity are paramount. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) & 𝗔𝗴𝗲𝗻𝘁𝘀: Explored how these patterns enhance foundation models with external tools and knowledge, focusing on strategies for efficient memory management and rigorous performance evaluation. 𝗙𝗶𝗻𝗲𝘁𝘂𝗻𝗶𝗻𝗴: Dived into the principles of adaptation, covering techniques like #PEFT and #LoRA (Low-Rank Adaptation), and the importance of selecting the right frameworks and base models for maximizing model performance and safety. 𝗔𝗜 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Gained a deeper understanding of designing successful AI products by integrating user feedback, and implementing robust observability (metrics, logs, traces) to debug and ensure reliability in production environments. This journey confirms that successful AI engineering requires a holistic approach, blending model capability with thoughtful system design. Excited to apply these research-backed principles in my next projects! #AIEngineering #MachineLearning #MLOps #PromptEngineering #ContinuousLearning #DataScience

💡 What if you could train AI models 11× faster using 90% less energy? I just open-sourced Adaptive Sparse Training (AST) - a production-ready system that does exactly that. 🎯 Results on CIFAR-10: ✅ 61.2% accuracy ✅ 89.6% energy savings ✅ 11.5× speedup ✅ 10.5 min vs 120 min training 🔬 How it works: A PI controller automatically selects the 10% most important samples each epoch. The system uses: • EMA-smoothed threshold adaptation • GPU-optimized batched processing • Real-time energy monitoring • Comprehensive error handling 💰 Real-world impact: • $100K GPU cluster → $10K training costs • 90% reduction in carbon footprint • Enable training on consumer GPUs • Potential billions in savings at scale 🚀 Why this matters NOW: Green AI is critical. As models scale (GPT-4, Claude, Gemini), energy costs are becoming prohibitive. This approach offers a path to sustainable AI development. 📦 Production-ready & open-source: https://lnkd.in/gETDkKN5 850+ lines of documented PyTorch code. Works on Kaggle free tier. MIT License. 🤝 Seeking collaborations for: - ImageNet scaling (50× speedup target) - Language model pretraining - Production ML pipeline integration - Research paper publication If you're in ML infrastructure, Green AI, or large-scale training - let's connect! #AI #MachineLearning #GreenAI #Sustainability #Innovation #OpenSource #PyTorch ⚠️ Important note: This is proof-of-concept on CIFAR-10 with unoptimized baseline. Validation at scale (ImageNet, LLMs) is the critical next step. See honest limitations in the repo. ⭐ Star the repo if you find this useful! 💬 What's your biggest challenge with ML training costs?

Hey AI—What’s New? Tech Talk in 60 Seconds or Less Anthropic just launched Claude Haiku 4.5, a faster and more affordable version of its popular AI assistant—now available to free users. 💨 Speed: Works more than twice as fast as the previous model 💡 Smarts: Writes and fixes computer code as well as Anthropic’s top model 💰 Cost: Uses only about one-third the computing power, meaning it’s cheaper and greener to run 🛡️ Safety: Designed to make fewer mistakes and avoid harmful responses Why it matters: This update makes advanced AI tools easier to access for students, educators, and everyday users—not just tech pros. Think of Haiku 4.5 as a lightweight, lightning-fast version of a powerful AI—built to help more people, faster.

🚀 Leveling up my AI/ML skills by building something for a better planet! * I just finished a major research project focused on making large AI models (LLMs) green and fast using my own laptop! * Most people focus on model performance, but I tackled the huge problem of LLM energy use and carbon footprint. * Here's the cool stuff I built: 1. The Problem I Solved: How do you run a powerful model like Llama 3 8B on a small device (like a phone or a laptop) without freezing it or wasting energy? 2. The Tech Deep Dive: I used Quantization-Aware Training (QAT)—a fancy way of shrinking the model's brain to a fraction of its size—making it light enough for Edge Devices. 3. The Intelligence Layer: I designed a "Smart Brain" called a Deep Reinforcement Learning (DRL) agent. This agent is trained to decide, in real-time, where to run the model (on the device or in the cloud) based on three things: i)Speed (Latency) ii)Battery Use (Energy) iii)Clean Energy Availability (Carbon Intensity) 🌍 The Outcome: My simulated system successfully cut the carbon footprint by X% compared to traditional methods! This project taught me the true complexity of designing Sustainable AI systems. Excited to share the paper soon! #AIProject #DeepLearning #GreenAI #MLOps #EdgeComputing #Quantization

Ever wondered how the next game-changing breakthrough will emerge? 🎯 The convergence of AI development tools and university computing power is reshaping how innovation happens. MIT's new TX-GAIN supercomputer delivers 2 AI exaflops - making it the most powerful AI system at any U.S. university. 📊 Three key enablers changing the game: • Conversational development through agentic IDEs • University-scale AI infrastructure democratizing research • Real-world applications beyond chatbots emerging Meanwhile, AWS launches the Bedrock AgentCore MCP Server, transforming how developers build AI agents. What previously required weeks of setup - learning services, managing security, deploying to production - now happens through simple conversational commands with coding assistants. Through AWS Bedrock AgentCore, enterprises can accelerate from prototype to production AI solutions while maintaining enterprise-grade security and scalability. This isn't just about faster development cycles. From protein modeling for biological defense to optimizing Air Force flight scheduling, we're seeing AI tackle problems that seemed intractable just months ago. 🔥 This is pure gold: Don't miss it https://lnkd.in/gxnuvcjk What breakthrough will your organization unlock when AI development becomes as simple as having a conversation? #GenerativeAI #SupercomputingInnovation #AIInfrastructure #EnterpriseAI #TechInnovation

Shadow AI in Higher Ed: A Growing Governance Challenge As AI tools become increasingly accessible, and pervasive, schools are witnessing the rise of "Shadow AI": the unsanctioned use of artificial intelligence by faculty, staff, and students outside official IT governance. From ChatGPT-powered grading assistants to rogue predictive analytics models, these tools often operate without institutional oversight, raising serious concerns around data privacy, academic integrity, and compliance. The root causes? A mix of innovation hunger, administrative bottlenecks, and the rapid democratization of AI. Faculty and staff are eager to experiment but centralized IT often struggles to keep pace with demand. Meanwhile, students are leveraging AI to enhance learning (or bypass it altogether) without clear guidelines. Institutions must shift from reactive control to proactive enablement. That means: * Establishing clear AI governance frameworks that balance innovation with accountability. * Creating AI sandboxes where experimentation is encouraged under safe, monitored conditions. * Educating stakeholders on ethical AI use, data stewardship, and institutional policies. * Collaborating across departments to align AI initiatives with strategic goals. Shadow AI isn’t just a risk; it’s a signal. It tells us where innovation is happening and where governance needs to evolve. By embracing responsible AI practices, higher ed can harness this energy while safeguarding its mission. Let’s move from Shadow to Strategy. #CIO #HigherEd #AI #EdTech #Governance #DigitalTransformation #ShadowAI

🚀The Future Belongs to the Upskilled In tech, nothing ages faster than yesterday’s knowledge. Every line of code, every algorithm, every framework — evolves. And so must we. Today’s focus: Upskilling with Intent. Not just learning a new tool, but understanding how it integrates into real-world systems: - How AI reshapes decision-making.   - How embedded systems bridge the physical and digital.   - How data transforms from numbers to narratives.  I’m not upskilling to stay relevant —   I’m upskilling to stay irreplaceable. Because in this era of exponential change,   your competitive edge isn’t your degree —   it’s your ability to adapt faster than the system evolves. 🔧 Skill of the Day : Reinforcing my foundation in AI-driven embedded systems   💡 Goal: Build something that connects intelligence with impact.  #Upskilling #AI #EmbeddedSystems #Industry40 #FutureOfTech #PreethiCiliveru #InnovationMindset

Microsoft’s new report shows: 86% of education institutions now use generative AI (the highest of any industry). Students using AI saw ~10% better exam results. 66% of leaders say they wouldn’t hire someone without AI literacy. AI is becoming a thought partner, not just a shortcut. Education is training the next generation to manage AI as colleagues, not just tools. If schools can reimagine learning with AI… what’s stopping businesses, governments, and finance from doing the same? #AI #EdTech #FutureOfWork #Fintech #Tokenisation #GenerativeAI

💡 Continuous Learning through Knowledge Sharing One thing I’ve realized as an AI/ML Engineer is that true growth doesn’t just come from solving problems—it comes from sharing what you’ve learned so others can build on it. Recently, I’ve been working on projects involving LLM-based applications, Retrieval-Augmented Generation (RAG), and multi-agent AI systems. Each challenge taught me valuable lessons about scalability, performance, and the importance of aligning AI systems with real-world business needs. 🌍 Sharing knowledge—whether through posts, open-source contributions, or mentoring—not only reinforces my own understanding but also creates space for collaboration and innovation in the AI community. 🤝 I’d love to hear: how do you share your learnings with your peers or professional network? 📩 Reach me anytime at jaganadari0825@gmail.com 📞 +1 (806) 429-2130 #AI #MachineLearning #KnowledgeSharing #GenAI #LLM #RAG