Architectural Design Decisions for Managing Features, Metadata, and Models in Machine Learning Platforms

The increasing popularity of Machine Learning (ML) has prompted companies to adopt it to enhance their business processes and generate additional value. To leverage ML, companies must build robust platforms for ML training and serving pipelines/systems. Three key components of such platforms are the feature store, model repository, and metadata store that enable developers to store, share, govern, and discover features, models, and metadata. Nevertheless, implementing and using these components is challenging, as platform developers and users must identify potential design options and their dependencies, and select the most appropriate options by balancing competing quality trade-offs. To support the systematic development and use of these platform components, this paper presents a framework comprising 15 Architectural Design Decisions (ADDs), 76 decision options, and 40 decision drivers, based on a review of 44 industrial gray literature sources.