feast VS kedro-great

Feast stands as an exceptional open-source feature store, revolutionizing the efficient management and uninterrupted serving of machine learning (ML) features for real-time applications. At its core, Feast offers a sophisticated interface for storing, discovering, and accessing features—the individual measurable properties or characteristics of data essential for ML modeling. Operating on a distributed architecture, Feast harmoniously integrates several pivotal components, including the Feast Registry, Stream Processor, Batch Materialization Engine, and Stores.

AI Engineering is basically Data Engineering focused on AI. When in "traditional" Data Engineering you create pipelines that store processed data in something like a Data Lake, in AI Eng. your end storage might be a specialized Feature Storage (like Feast or GCP Vertex AI).

There are some AI Engineers with strong scientific/mathematical background, but that's rare. Usually, you're paired with these ML people that actually develop and evaluate the models.

So my advice is to start with Data Engineering and then find a specialization AI. You should have a VERY solid foundation on scripting and programming, specially Python. Also, a lot of concepts of "data wrangling". Understanding how data flows from point A to point B, how the intermediate storages and streaming engines work, etc. Functional programming is key here.

[0] https://github.com/feast-dev/feast

A feature store usually stores features which are used for training ML model. It is a centralized place for collaboration between data engineer, ML engineer, and data scientist, so that data engineer can write to the feature store while ML engineer and data scientist read from it. Hopsworks https://www.hopsworks.ai and feast https://github.com/feast-dev/feast are examples of open source feature store.

Github: https://github.com/feast-dev/feast

Have you looked at Feats as a Feature Store solution? It seems promising but I haven't really looked into it yet though.

I expected Great Expectations library to be recommended, but nobody told anything. Instead, unit testing and/or smoke tests using pytest. And checking them with Jenkins. Anyway, if Kedro ends up being our project template, I'll keep an eye on the plugin with Great Expectations.