Organisations of today are investing more and more into Machine Learning capabilities, but as they grow, they often struggle to scale their data science workflows.
One of the key problems is the inability to share features between training and inferencing pipelines, as well as across different machine learning projects. This friction inturn translates to increased time to market.
This is where feature stores can come to the rescue by providing an ability to curate, track and share features across different data science projects and also ensure parity between training and serving layers. They can also empower your organisation by providing a decision vault which can be used to justify historical decisions and comply with compliance & regulations.
When designing your feature store, it’s important to understand that the idea of feature stores is to increase reusability and reduce duplication. At the same time, it should provide transparency to its users to view the logic that creates the feature lineage to trace the upstream sources. Of course, there is no one design that fits all as every organisation has its own sets of priorities and challenges.
If you are looking to get started and you are keen to learn how we can help in your journey of scaling machine learning models, please get in touch.
Disclaimer: The statements and opinions expressed in this article are those of the author(s) and do not necessarily reflect the positions of Thoughtworks.