Storing features
Feature stores are essential in providing a centralized platform for the management, storage, and serving of engineered features across multiple machine learning projects. They play a crucial role in enhancing consistency, reducing redundancy, and accelerating the model development and deployment process. By organizing and making features readily accessible, feature stores such as Feast ensure that LLMOps best practices are followed by efficiently retrieving and utilizing preprocessed features for LLM training, thereby streamlining the machine learning workflow and supporting scalable solutions.
Let’s look at an example for storing data related to tokens, token_ids, and attention_mask in Feast. This involves creating a new Feast project to accommodate the storage of tokenized text data and its associated metadata for LLM training and inference:
feast init token_feast_project cd token_feast_project
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