Comet Artifacts is a toolset designed to address the complexities of managing machine learning experiment data by providing a structured way to log, version, and browse data across experimentation pipelines. It facilitates the management of outputs such as model predictions and weights, which can serve as inputs for subsequent experiments, by creating versioned objects called Artifacts. These Artifacts are immutable snapshots organized in a logical structure, with metadata, version numbers, tags, and aliases to track their lineage across experiments. This setup enables machine learning teams to construct multi-stage pipelines or Directed Acyclic Graphs (DAGs), ensuring centralized and versioned access to intermediate data. Comet Artifacts simplifies the process of registering and using Artifacts in experiments, requiring minimal lines of code for integration, thereby streamlining data management and enhancing collaboration.