Post Deployment Data Science with Wojtek Kuberski #56
AI Stories
Navigating Model Monitoring in Product Development
This chapter explores the transition from freelance work to launching a product company centered on model monitoring in data science. Key challenges discussed include covariate shift and concept drift after model deployment, highlighting the necessity of ongoing performance evaluation. Through relatable examples, the importance of continuous monitoring and the need for high-quality data is underscored to ensure effective model retraining and performance sustainability.
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