Machine learning models have become ubiquitous in modern applications. The ML Lifecycle describes a three-phase process used by data scientists and data engineers to develop, train, and serve models. Unfortunately, context around the data, code, people, and systems involved in these pipelines is not captured today. In this paper, we first discuss common pitfalls that missing context creates. Some examples where context is missing include tracking the relationships between code and data and capturing experimental processes over time. We then discuss techniques to address these challenges and briefly mention future work around designing and implementing systems in this space.