Скачать или смотреть Why ML Models Fail in Production | Real-World MLOps Problems

🚨 "95% Accuracy in Jupyter Notebook... but everything FAILED in Production?"
You’re not alone — over 85% of machine learning models either never make it to production or collapse once deployed.

In this beginner-friendly video, we break down why ML models fail in the real world, even when they look perfect during training. If you're a data scientist, ML engineer, or just starting with MLOps — this video is your survival guide! 💥

🔍 What You’ll Learn:
✅ What is Data Drift and Concept Drift
✅ Why models degrade without Monitoring
✅ The importance of Version Control for datasets and models
✅ How missing feedback loops silently destroy accuracy
✅ Real-world deployment issues: dependencies, config errors, and bias
✅ How MLOps tools like Docker, FastAPI, Prometheus & DVC solve all this

🧠 Whether you're working on a side project or building enterprise-grade AI systems, knowing why models fail is the first step to doing ML right. This is your wake-up call from the Jupyter dreamland. 🛑