Why Keyword Search Alone Won’t Scratch the Search Itch

Knowledge graphs are becoming the retail brain. In my current project, they connect: What’s in stock What’s selling What’s moving What’s at risk Paired with GenAI, they turn complex inventory data into real answers. #AI #SupplyChain #RetailTech

Traditional inventory systems track stock. We’re building one that understands it. Knowledge graphs help us model: > Supplier delays > Promo campaigns > Freight constraints > Forecast shifts This is real-time inventory intelligence. #AI #RetailOps

Imagine asking: "Which products at risk from a supplier delay affect our top 10 stores next month?" Spreadsheets can't answer that. Graphs can. We're using #KnowledgeGraphs + #GenAI to turn inventory data into decisions. #RAG #AIinRetail

I’m working on a project using knowledge graphs to fix stock imbalances. Overstock in one region. Stockouts in another. The issue? Disconnected data. Now we link inventory, demand, logistics, and promos to recommend dynamic rebalancing. #RetailTech #InventoryOptimization

Inventory isn't just a spreadsheet problem. It's a graph problem. Most retailers don’t know how a supplier delay in one region affects promos in another. We’re using a knowledge graph to connect products, warehouses, suppliers & demand. #GenAI #KnowledgeGraphs #RetailAI

Navigating the AI Ethics Landscape: A Call for Responsibility https://t.co/yLR6HMazzS

The Philosopher's Stone for Data Scientists: The Quest for Ultimate Insight. https://t.co/SjlVk9l02P

Curious about what's actually happening an LLM's attention mechanism? Check out our latest guide to attention visualization with BertViz!

Check out my latest video on developing a Flask web app and integrating with Mailtrap for personalized email sending. Also, learn how to store data and predictions in MongoDB. #Flask #Mailtrap #MongoDB https://t.co/f1PFlKuOl6

Mastering ML Deployment Myths around ML deployment can mislead your strategy. Embrace continuous updates, scalability, and the power of multiple models to drive success. #MachineLearning #AI #TechTruths

Debunking ML Deployment Myths From deploying multiple models to frequent updates and scalability concerns, understanding the realities of ML deployment is key to success. Stay informed and keep your models performing optimally. #MLMyths #AI #DataScience

Importance of Scaling in ML Thinking scalability isn't a concern for ML engineers is a myth. Efficiently managing large datasets and real-time predictions requires robust scaling strategies. #MachineLearning #BigData #TechMyths

Myth: Most ML Engineers Don’t Need to Worry About Scale Scalability is crucial for ML engineers. Handling large datasets and ensuring performance at scale are essential for successful model deployment. #Scalability #MLDeployment #AI

Do you remember when you joined X? I do! #MyXAnniversary

The Need for Frequent Model Updates Contrary to the myth, ML models require regular updates to stay effective. New data and changing objectives demand continuous retraining and adjustments. #MachineLearning #ModelMaintenance #TechMyths

Myth: You Won’t Need to Update Your Models as Much In reality, frequent model updates are necessary to keep up with new data and evolving business needs. Regular retraining ensures relevance and accuracy. #MLUpdates #AI #DataScience

If you cannot rely on your users to create reliable prompts, make sure you know how to make good system messages. System prompts simplify prompt engineering, automate context setting, and improve output clarity. Understand how to: https://t.co/AC0F8QdDbs

Reality Check: Model Performance Over Time Assuming model performance remains constant is a myth. Regular updates and retraining are essential to combat model drift and ensure continued effectiveness. #MachineLearning #ModelUpdate #TechMyths

Myth: If We Don’t Do Anything, Model Performance Remains the Same ML models degrade over time due to changing data distributions. Continuous monitoring and updating are crucial to maintain accuracy and reliability. #ModelDrift #MLMaintenance #AI