This document discusses asset management challenges for machine learning models. It notes that ML can increase productivity by 50x but achieving this requires addressing hidden complexities, such as data and model dependencies. The document reviews several open source solutions for managing ML workflows, models, code, data and deployments. These include Seldon.io for serving ML, Vespa for low latency serving with data, DVC for versioning code and data, and PipelineAI for reproducible model pipelines and serving. However, the document notes there is no single solution and integrating components remains challenging due to the interconnected nature of ML systems.