There is a need for consolidation of multi-modal datasets obtained from sensors within AM machines, build planning, and post-build imaging and analysis, going through the entire AM pipeline. We propose a database repository, built on the FAIR principles, to assist the application of machine learning tools that can harness the immense amount of data that has already been generated and set up protocols to deal with future data generation. This ranges from powder characteristics to build data to part analysis. The challenges of effectively using machine learning tools often come from a lack of sufficient and structured data. Enabling this structure throughout the AM pipeline will set a standard for future ML analysis and by knitting together disparate data sources, we can facilitate data fusion and analysis across the entire AM pipeline. We address the challenges of storage capacities, file naming conventions, and data retrieval for analysis.