#71 Scaling Machine Learning Adoption: A Pragmatic Approach
DataFramed
Operationalizing Machine Learning: Challenges and Strategies
This chapter explores the difficulties organizations encounter in implementing machine learning models, stressing the need for clear goals and metrics for success. It emphasizes a results-oriented approach in data science while drawing parallels between martial arts and effective machine learning practices. The discussion cautions against theoretical rigidity and highlights the importance of urgency, ethical considerations, and practical outcomes in MLOps.
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