Model Testing Essentials
Robustness in machine learning models requires rigorous testing akin to traditional software, emphasizing the importance of data quality. Defensive coding and preventive measures are crucial, as data scientists and ML engineers are closest to the data and must ensure its integrity. Key practices include monitoring for data type consistency and shifts in categorical distributions to prevent model degradation over time.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Real-Time ML Workflows at Capital One with Disha Singla - 606
Related Questions
Is data quality overlooked in machine learning?
Tell me something about testing as a tool in the context of the episode "Build a Culture of ML Testing and Model Quality // Mohamed Elgendy // MLOps Coffee Sessions #76" and the clip "Testing Insights."
Tell me something about testing as a tool in the context of the episode Build a Culture of ML Testing and Model Quality // Mohamed Elgendy // MLOps Coffee Sessions #76 and the clip Testing Insights.