How to Validate Your Model & Data and Easily Avoid Common ML Pitfalls

Explore the challenges and solutions in building stable machine learning models in this 52-minute video presentation. Learn about common pitfalls such as dataset biases, data leakages, quality issues, and model performance instability. Discover real-life examples of these faults and gain insights into creating effective validation tests. Follow along with a hands-on demonstration of running validation tests during the ML research phase. Acquire knowledge on identifying critical issues and implementing efficient tools to avoid them, ultimately improving your machine learning model development process.

Introduction
ML Failures and Motivation
ML Validation/Testing
Deepcheck Packages
Live Code Example
QnA