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Watch a 33-minute conference talk from the Toronto Machine Learning Series where Christian P. Calderon, MLOps & Deployment Engineer at Zapata AI, explores the challenges and solutions for implementing AI and ML systems with real-time sensor data in edge environments. Learn practical approaches to handling real-time data cleaning, transformation, and integration with historical data, while managing power-intensive models on-premises. Through a detailed case study of Zapata AI's race strategy analytics work with Andretti Global, discover transferable insights and architectural principles for building robust data infrastructure that supports real-time analytics across various industries.
