Introducing efsync, an open-source MLOps toolkit for syncing dependencies and model files to AWS EFS for serverless machine learning.
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.
