Data Preparation Essentials
Data preparation is a critical phase in machine learning, involving extensive cleaning and transformation of data. Key strategies include splitting data into training, validation, and test sets to monitor model performance and prevent overfitting. Feature engineering emerges as a vital component, with specialized companies dedicated to optimizing this process, showcasing its importance in building effective machine learning pipelines.In this clip
From this podcast
MLOps.community
MLOps Coffee Sessions #10 Analyzing the Article “Continuous Delivery and Automation Pipelines in Machine Learning" // Part 2
Related Questions