Navigating Machine Learning Challenges

This chapter explores the intricacies of deploying machine learning models, particularly BERT, and the surprises that arise when simpler models outperform more complex ones. It emphasizes the importance of evaluating the necessity of machine learning for specific problems, addressing the challenges of model performance and the significance of data quality. The discussion also covers the collaborative nature of ML teams and the need for thorough quality assurance in critical applications like fraud detection.