In this 49-minute InfoQ talk, discover the five common pitfalls that lead to Machine Learning project failures and practical strategies to overcome them. Learn from an experienced ML engineer's insights on how to increase your chances of success and deliver real-world impact with your ML initiatives. Explore effective MLOps practices and software engineering approaches that can transform struggling ML projects into successful implementations.
Overview
Syllabus
Why Most ML Projects Fail (and How to Fix It)
Taught by
InfoQ
