CARA ML Feature Store

What CARA ML Feature Store is

CARA ML Feature Store manages, stores, and serves machine learning features for model training and prediction. It provides online and offline stores, with serving modes for both. The product makes offline data available for real-time predictions without requiring custom pipelines. It also serves the same data during training and inference, addressing training-serving skew.

The Feature Store is part of CaraML’s open source MLOps platform. It is intended to help data science teams run solutions at scale without engineering expertise, while standardizing data engineering workflows across models and teams. Its infrastructure approach uses cloud managed services and does not require teams to deploy and manage dedicated infrastructure. The listed deployment model is self-hosted.

The maker links to documentation and a Get Started page for setup guidance. Its public GitHub repository is described as the Feature Store for the CaraML Platform and includes modules for protobuf, PySpark, registry, SDK, serving, Spark, and test utilities. CARA ML Feature Store is listed as free and open source. It may suit teams that want a shared way to manage feature data across training and inference, provided its self-hosted deployment model fits their environment.

Who it suits

CARA ML Feature Store suits data science teams that need online and offline feature data for training and prediction. It is also aimed at teams seeking standardized data workflows across models without managing dedicated infrastructure deployment themselves.

Getting started

1. Visit https://caraml.dev/featurestore/.
2. Open the linked documentation or Get Started page.
3. Review the public GitHub repository and its Feature Store modules.
4. Set up the self-hosted deployment on cloud managed services.

Verdict

Choose CARA ML Feature Store if your team wants an open source feature store for both online and offline data, with consistent inputs for training and inference. Its cloud managed services approach avoids managing dedicated infrastructure, while deployment is listed as self-hosted. Look elsewhere if self-hosted deployment does not suit your team.