Open Internet by MindsNet

Balancing AutoML Benefits and Drawbacks in ML Workflows

The use of AutoML in real-world machine learning workflows presents trade-offs between faster experimentation and clarity on model behavior, easier entry into ML and harder debugging, and abstraction in prototyping and production monitoring. These trade-offs indicate a need to better understand where AutoML adds value and where it introduces friction.

Computing & Technology, Computer Science, Machine Learning