Data Processing Pipeline
The discussion dives into the complexities of processing vast amounts of data for machine learning. Key insights reveal the meticulous steps involved, from initial sampling to feature engineering, and the challenges of training models that can take weeks to yield results. The importance of efficient data handling and iteration is emphasized, showcasing the intricate balance between data size and model performance.In this clip
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The AI Podcast
Credit Check: Capital One’s Kyle Nicholson on Modern Machine Learning in Finance - Ep. 114
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