This is an implementation of a feature store in order to have a tool to keep track of the features that going to be used in the process of building and training Machine Learning models. This feature store was built on top of Feast.
Feast is an operational data system for managing and serving machine learning features to models in production. Feast is able to serve feature data to models from a low-latency online store (for real-time prediction) or from an offline store (for scale-out batch scoring or model training).
The architecture of the feature store of this project is the following:
Machine Learning Engineer, passionate about artificial intelligence and software development, I am currently exploring the world of MLOps, researching and gaining more and more knowledge on this topic. I am very interested in cloud computing and currently have experience working with AWS and GCP. 🐨 🚀