In many enterprises the biggest bottleneck isn’t AI modeling—it’s embedding intelligence into workflows. Consider the concept of “agentic AI”—systems that don’t just predict, they act across workflows. McKinsey & Company+1 Here’s a practical architecture I see working: Build a business semantics layer that links your KPIs, domain logic and data context. arXiv Deploy AI agents on that layer to automate multistep workflows—not just analytic tasks. Monitor both model performance and workflow outcomes (time saved, error reduced). In your next project: ask not only “Is the model accurate?” but “Is the workflow live and operational?” #AdvancedAnalytics #AgenticAI #DataArchitecture #charan #Oct24

If you’ve ever spent days tweaking epochs, learning rates, and data splits, you know how rare this moment is. 💡 Here’s why it matters: Fine-tuning doesn’t always mean better. It only works when: Your dataset is high-quality and domain-aligned The base model isn’t already saturated You use proper validation (train/test separation is non-negotiable) When it finally clicks, it’s not luck, it’s alignment between data, architecture, and intent. Celebrate it. You earned that F1 score bump. 🎯 🎥 Subscribe to The Neurl Creators YouTube Channel for more AI humor that actually teaches you something and get more AI related insights from our Substack page: https://lnkd.in/dWqQJeye #AI #MachineLearning #FineTuning #DataScience #LLMs #NeurlCreators

In a recent Snowflake whitepaper titled “Deploying AI Agents at Scale: 3 Patterns to Move Beyond POCs”, I came across a well-defined framework for thinking about how organizations can operationalize AI beyond the pilot stage. Here are the three core agent types the paper highlights: 1️⃣ Data Agents - Designed to combine data and tools efficiently, delivering data-grounded insights with a strong emphasis on accuracy and trust. 2️⃣ Conversational Agents - Focused on interacting with humans naturally, providing informed, context-aware responses to queries or tasks. 3️⃣ Multi-Agent Systems - Built to orchestrate multiple specialized agents, enabling complex workflows where each step may require different expertise or data retrieval. What stood out to me is how these patterns align with real-world enterprise needs - from data reliability to collaboration between intelligent systems, this seems to be the direction AI architecture is evolving toward. 💡 As organizations move from experimentation to implementation, understanding these agent patterns could be key to building scalable, production-ready AI systems. #AI #Snowflake #AIAgents #DataEngineering #EnterpriseAI #MachineLearning #GenAI

The Great MLOps Pivot: Why Data is the real AI moat? For years, AI teams have focused too much on building fancy models - new architectures, better fine-tuning & endless benchmarks. But once these models go live, they often fail because real-world data keeps changing. The real game-changer isn't a new model - it's shifting from model-centric to data-centric AI. In a model-centric world, the model is everything and data is just fuel. In a data-centric world, data becomes the main asset - it's cleaned, improved, and updated to make models work better over time. Data-centric MLOps means building systems that keep learning and improving. It's powered by three key ideas: -> Feedback Loops: Learn from how users interact - what they like, ignore or correct. Detect when data or predictions start drifting off. -> Data Curation: Treat data like code. Version, track and validate it using tools like DVC or Pachyderm. -> Continuous Retraining: Automate retraining when performance drops or new data arrives - with safe rollouts. This turns static, fragile models into living systems that get smarter every day. As Andrew Ng says - improving data quality gives a far higher return than endlessly tweaking models. Start small: pick one feedback signal and start logging it. That's the first turn of your data flywheel. Stop building fancy models. Start building systems that learn. #MLOps #DataCentricAI #AI #MachineLearning #DataScience #AIML

GenAI can’t use what it can’t understand. Most organizations are drowning in document debris — unstructured content that limits AI’s potential. At Enterprise AI World, Heather Eisenbraun will show how Information Architecture turns that chaos into clarity, powering GenAI systems that actually deliver business intelligence. 🧭 The GenAI Content Crisis: How IA Transforms Document Debris into Business Intelligence 📍 Thursday, November 20, 2pm, at Enterprise AI World (part of KMWorld) https://hubs.ly/Q03SQmzw0 #GenAI #InformationArchitecture #KnowledgeManagement #EnterpriseSearch #ContentStrategy #AITransformation

At NewTek Solutions, a Planview Business Partner, we know that training powerful models is only part of the equation. For autonomous agents to make smart decisions, they rely on clean, consistent, and real-time data. This quick guide unpacks the architecture behind reliable AI systems — and reveals what happens when data foundations are overlooked. Flip through to discover why strong data foundations are essential for responsible AI. 🔗 Read more: https://okt.to/nk6wIA #SmartData #BusinessIntelligence #FutureofAI

Ground Truth Data Achieving 90%+ accuracy in any AI model needs more than clean code and clever prompts. The secret ingredient is the raw material — ground truth data! Without high-quality labeled examples, the best architecture will still guess in the dark. Ground truth is what teaches your AI what “right” actually looks like. It’s the silent backbone that turns experimentation into measurable improvement. Models don’t learn from magic - they learn from truth. And in AI, that truth must be defined, labeled, and protected like gold. #groundtruth

⚙️ Hybrid AI Log Classification - Optimized for Cost, Speed & Scalability 🚀 ⚡ “Before the client knows, the system already does.” Every modern system generates thousands of logs per second. But the real challenge isn’t collecting them — it’s understanding which ones matter, and doing it before the client notices an issue. That’s where Hybrid AI Log Classification comes in. We designed an intelligent pipeline that combines the strengths of multiple AI layers — each chosen for the right balance of speed, accuracy, and cost efficiency. 🧠 The Architecture in Action 1️⃣ BERT + DBSCAN for Clustering Groups similar logs automatically, uncovering new or unseen error families. 2️⃣ Regex-based Classification Handles repetitive, fixed-format logs in milliseconds — lightning-fast and cost-free. 3️⃣ BERT + Logistic Regression Activates when enough labeled samples exist — providing a high-accuracy, low-latency classification for complex patterns. 4️⃣ LLM Few-Shot Classification Steps in for rare or unseen errors with minimal examples — understanding the context even with little data. 5️⃣ Alert & Visualization Layer Sends categorized alerts to clients instantly, long before customers experience any disruption. ⚙️ Why Hybrid Matters Not all intelligence needs to be expensive. By combining rule-based, machine learning, and LLM-driven reasoning, this system ensures: Speed for frequent logs Precision for complex logs Adaptability for new patterns Scalability across environments Cost optimization through selective model usage 💡 Final Thought True operational intelligence isn’t just automation — It’s knowing which intelligence to apply, when. Proactive, intelligent, and cost-aware — that’s the future of log monitoring. 🔔 Special thanks to Dhaval Patel for inspiring so many of us in the AI & Data Engineering community to think deeply about real-world problem solving, not just theory. 🙌 #AI #MachineLearning #AIOps #Observability #LogMonitoring #Automation #BERT #LLM #MLOps #Innovation #Engineering #DataScience #Codebasics

As AI moves from simple automation to true autonomy, the foundations beneath it need to advance as well. Architecting in the Age of Intelligence looks at how data architectures rooted in trust, adaptability and context are shaping the next generation of intelligent and scalable systems. This article also marks the start of our Future of Data Architecture series, where we explore how ideas such as Knowledge Architecture and Governance by Design are changing the way organisations build for real world, trustworthy AI. Read the full article here 👇 👉 https://okt.to/fOuSyN Authors: Mauro Confalone, Amit G., Binodanand Mishra #AI #DataArchitecture #EnterpriseArchitecture #IntelligentSystems #Capco

What are the building blocks of an AI agent? > See one built live in Snowflake Cortex in our on-demand recording: https://lnkd.in/gX29tGWr Carlos Bossy also breaks down what it takes to move from experimentation to reliable execution, covering:   • Data foundations • Semantic models • Orchestration patterns • Agent design principles #AgenticAI #datamodeling #Cortex