Robust MlOps infrastructure
Building a Robust Machine Learning Operations Infrastructure Creating a reliable and flexible MLOps infrastructure is critical for organizations leveraging machine learning in production. In this post, I’ll explore practical challenges that emerge when deploying ML systems and propose pragmatic solutions using open-source tools and standard workflows. The Challenge of Production ML Systems After deploying a machine learning model in production, a new set of challenges emerges. The model pipeline typically involves data storage (like S3), orchestration tools (such as Airflow, Prefect, or Flyte), and back to storage for processed results. But this simple flow becomes complex when we consider the full ML lifecycle. ...