Machine learning model to production

This document discusses moving machine learning models from prototype to production. It outlines some common problems with the current workflow where moving to production often requires redevelopment from scratch. Some proposed solutions include using notebooks as APIs and developing analytics that are accessed via an API. It also discusses different data science platforms and architectures for building end-to-end machine learning systems, focusing on flexibility, security, testing and scalability for production environments. The document recommends a custom backend integrated with Spark via APIs as the best approach for the current project.Read less