In a Machine Learning (ML) system, characteristics of the ML components create new challenges for software system design and development activities. Data-dependent behavior causes risks in ML systems. Dealing with such risks in the development phase requires non-trivial costs due to un-controllable data generation processes in the test phase. In addition, ML systems often need continuous monitoring and validation in run-time. In this paper, we demonstrate the risk of ML systems because of the unknown data generation processes and model uncertainty and propose an integrated dependency tracking system that balances the cost and risks in the development stage and operation stage. Our solution uses blockchain to track the co-evolution of the models and the corresponding datasets. As blockchain provides a transparent and immutable data store, the provenance of data and models stored on the blockchain provides a trustworthy trace for dependencies between datasets and models at the development phase, and predictions at the operation phase. A graph database is further used to store and visualize these dependencies for risk mitigation. We evaluate the technical feasibility of our solution using a real-world scenario, including machine-learning models for distinguishing beef produced from different farms in Australia.