Kaggle, ML Community / Engineering (Sanyam Bhutani)
Machine Learning Street Talk (MLST)
00:00
From Kaggle to Production: Bridging the ML Divide
This chapter explores the challenges organizations face when transitioning machine learning models developed in Kaggle competitions to real-world production environments. It discusses the gap between competition practices and practical applications, including issues like data set drift and the need for collaboration between data scientists and ML engineers. Additionally, it highlights the evolving nature of Kaggle competitions, the importance of feature engineering, and the differing strategies between academia and industry.
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