Why Your ML Models Fail in the Real World (And How to Fix It)

Описание: Machine Learning in industry is vastly different from what is taught in school. While academia focuses on squeezing out every bit of model performance, production systems must prioritize reliability, scalability, and maintainability. In this video, we explore the entire ML lifecycle, moving beyond the algorithm—which is actually just a small part of a functioning system—to look at the interface, data stack, and infrastructure.
We dive into the iterative process used by top-tier companies to build production-ready applications. You will learn why most models fail silently in the wild and how to combat the "silent killer" of AI: Data Leakage. From data engineering fundamentals to the "hashing trick" for managing millions of features, this guide provides a blueprint for building AI that survives the real world.
What we cover:
• Why "Static" datasets are dangerous for production.
• The Data Science Hierarchy of Needs.
• How to handle class imbalance and data bias.
• Mastering slice-based evaluation to find hidden model flaws.

#MachineLearning #MLOps #DataEngineering #AI #DataScience #ChipHuyen #MLSystems #ModelDeployment #FeatureEngineering #DataScienceTutorial #BigData #ArtificialIntelligence #SoftwareEngineering #MachineLearningSystems #TechEducation