Bridging the Gap Between ML Ops and DevOps

The chapter highlights the significant gap between ML ops methodology and traditional DevOps work, focusing on development, standardization, automation, and testing. It emphasizes the importance of moving machine learning models seamlessly from development to production and discusses the advantages of using notebooks for data science work. The conversation delves into organizing code into modular structures and integrating it with hosting services for production readiness.