Incorporate Feast into Llama Stack

[franciscojavierarceo]

🚀 Describe the new functionality needed

The Llama stack should leverage Feast to enable the Model Lifecycle.

Feast already plays an important role in the AI/ML Lifecyle in Kubeflow and the Llama Stack would benefit from using it as well (image below for reference).

💡 Why is this needed? What if we don't build it?

Why is this needed

Feast, the open source Feature Store provides important primitives for production AI/ML including support for Vector Databases. Feast is a widely used feature store and is an add on component to Kubeflow.

Feast plays a critical role in (1) serving data for production AI Applications, (2) enabling model developers to more easily build training datasets, and (3) building APIs for distributed scale (i.e., Kubernetes). Moreover, Feast already supports APIs for executing critical operations on data, including but not limited to:

1. Ingestion
2. Transformation
3. Indexing
4. Retrieval/Serving
5. Dataset Generation/Preparation
6. Data Governance

Feast supports many different offline data warehouses and online databases, which allows for flexibility for software engineers while giving Data Scientists/Machine Learning Engineers/Researchers (i.e., experts in building models) a consistent SDK for leveraging data in production systems.

Adding Feast as a component to the Llama Stack would allow flexibility in choosing data providers and leverage Feast's existing significant contributions in serving the needs of engineers and model builders.

*Documentation on "What is Feast?".

What if we don't build it?

I think inevitably many of the Feast patterns will be implemented in Llama Stack that exist in Feast (likely with slightly different implementations). In the limit, this ends up re-inventing the wheel or implementing a better wheel.

If folks want to implement a better wheel, the Feats community would be eager to incorporate the learnings into the project so the broader AI/ML community could also benefit.

Other thoughts

There are pros and cons.

Pros:

1. Complexity is abstracted away in Feast
2. Benefit from existing expertise and implementation
3. Have support for known challenges that occur during the model building lifecycle
4. Benefit from existing data integrations

Cons:

1. Complexity is not trivially visible in the Llama stack repository
2. Llama stack maintainers may not like every choice made in Feast and will have to work with Feast to make changes (I am a maintainer for Feast and can commit to being very collaborative here)

I'm sure there are more cons but I wanted to list some.