Model Versioning in Machine Learning Explained in 60 Seconds | What is Model Versioning?

Model versioning in machine learning is how teams track, compare, and safely update different versions of their ML models over time. In this 60-second glossary video, you’ll learn what model versioning is, why it matters for reproducibility, and how it supports reliable deployments in real-world ML systems. What you'll learn: - A clear definition of model versioning in machine learning - How versioning helps you reproduce past results and debug issues - A simple mental model for thinking about model versions - A concrete example from a production ML workflow - Why model versioning is essential for professional ML and MLOps Watch more AI & ML glossary videos from 1MinuteGlossary: - Model in Machine Learning Explained in 60 Seconds | What is a Machine Learning Model?: https://www.youtube.com/watch?v=ZJRe9Mljyws - Model Serving Explained in 60 Seconds | What is Model Serving in AI?: https://www.youtube.com/watch?v=yyFocU1RAZg - Data Distribution Shift in Machine Learning Explained in 60 Seconds | What is Distribution Shift?: https://www.youtube.com/watch?v=sRWmnuFdBuA - Cross Validation in Machine Learning Explained in 60 Seconds | What is Cross Validation?: https://www.youtube.com/watch?v=sfwef8rlvIA Stay tuned to 1MinuteGlossary for more fast, clear explanations of core AI and machine learning concepts. Chapters: 00:00 Intro 00:05 Plain-English Definition 00:15 Mental Model for Versions 00:32 Practical ML Example 00:50 Why Model Versioning Matters #ModelVersioning #MachineLearningModels #MlReproducibility #MlopsBasics #AiGlossary Watch Next: https://www.youtube.com/watch?v=ZJRe9Mljyws Check out the other playlists: https://www.youtube.com/playlist?list=PLg8mVVQENKnYcEb6yyd3uGPlektphmUZS Please post your questions below in the Comments -- I'll be happy to respond a.s.a.p. ------------------- Subscribe to get notified of our new videos: https://www.youtube.com/@1MinuteGlossary?sub_confirmation=1 ------------------- Thank you for watching! If you found this