Explore the critical importance of monitoring in machine learning success through this 42-minute conference talk from MLOps World: Machine Learning in Production. Delve into the key reasons why ML models can silently fail and lose predictive power, including model drift, data integrity issues, outliers, and bias. Discover how cutting-edge Explainable AI and model analytics can rapidly identify root causes of operational issues. Learn strategies for implementing effective MLOps practices, including model and cohort comparisons to reduce time to market for new models. Gain insights from Amit Paka, CPO and Co-founder of Fiddler AI, as he shares his expertise in ML monitoring and AI explainability to help build high-performance MLOps systems.
Build High Performance MLOps With ML Monitoring and AI Explainability
MLOps World: Machine Learning in Production via YouTube
Overview
Syllabus
Build High Performance MLOps With ML Monitoring and AI Explainability
Taught by
MLOps World: Machine Learning in Production
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