The Evolution of Feature Stores

Feature stores have become a vital part of the machine learning landscape, evolving out of the need for better data management in machine learning processes. Initially, many organizations found that machine learning was more data-driven than model-based, leading to confusion and varying approaches among different vendors. Some companies opted for traditional feature storage as tables, while others focused on defining features as code and building pipelines. Over time, the narrative around feature stores has shifted, moving from hype to a sustainable growth phase, with companies now acknowledging the importance of established practices for better integration into their workflows.