Explore the critical importance of monitoring in machine learning success through this 36-minute conference talk from MLOps World: Machine Learning in Production. Discover why ML models can silently fail and lose predictive power, focusing on key reasons such as model drift, data integrity, outliers, and bias. Learn how cutting-edge Explainable AI and model analytics can quickly identify root causes of operational issues, which are often time-consuming to fix. Gain insights into the iterative nature of MLOps and understand how model and cohort comparisons can reduce time to market for new models. Presented by Amit Paka, CPO and Co-founder of Fiddler AI, this talk offers valuable knowledge on building high-performance MLOps systems with ML monitoring and AI explainability.
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
Related Courses
-
Build High Performance MLOps With ML Monitoring and AI Explainability
-
Build High Performance MLOps With ML Monitoring and AI Explainability
-
MLOps (Machine Learning Operations) Fundamentals
-
MLOps Deployment and Life Cycling
-
Monitoring Machine Learning Concepts
-
Minimize Risk and Accelerate MLOps with ML Monitoring and Explainability
