Argues that data scientists should own the entire process from problem identification to solution deployment for greater impact and efficiency.
A machine learning engineer reflects on the gap between ML research and real-world production, emphasizing the critical importance of data over models.
A guide to best practices for monitoring, maintaining, and managing machine learning models and data pipelines in a production environment.
Explores six unexpected challenges that arise after deploying machine learning models in production, from data schema changes to organizational issues.
A case study on building a production ML system to predict patient hospitalization costs for Southeast Asia's largest healthcare group.
