CARA ML Feature Store
CARA ML Feature Store manages, stores, and serves machine learning features for model training and prediction.
What CARA ML Feature Store is
CARA ML Feature Store manages, stores, and serves machine learning features for model training and prediction. It has online and offline stores, and makes offline data available for real-time predictions without requiring custom pipelines. By serving the same data to models during training and inference, it addresses training-serving skew. The product standardizes data engineering workflows across models and teams, and is designed to run on cloud managed services without requiring teams to deploy and manage dedicated infrastructure. CaraML describes its wider platform as open source MLOps software for data science teams running solutions at scale without engineering expertise. The Feature Store uses a self-hosted deployment model and supports online and offline serving. Its public repository includes modules for protobuf, PySpark, registry, SDK, serving, Spark, and test utilities. The product is listed as free and open source, and its page links to documentation and a Get Started resource.
Good
- Provides both online and offline stores
- Makes offline data available for real-time predictions
- Serves the same data during training and inference
- Open source and listed as free
Watch out
- Uses a self-hosted deployment model
- Requires working with the CaraML platform
Who it suits
It may suit data science teams looking to standardize feature workflows across models and teams. Its stated approach is aimed at teams seeking to run solutions at scale without engineering expertise.
Verdict
CARA ML Feature Store focuses on consistent feature data across training and inference, with both online and offline serving. It is free and open source, but its listed deployment model is self-hosted.
The facts
Each one read from CARA ML Feature Store's own pages, with the date it was checked.
What it does
- Deployment
- It runs on top of cloud managed services and does not require deployment and management of dedicated infrastructure.checked 8 Oct 2026
- Documentation
- The Feature Store page links to CaraML documentation for getting started.checked 4 Oct 2026
- Infrastructure
- It runs on cloud managed services without requiring deployment and management of dedicated infrastructure.checked 4 Oct 2026
- Intended users
- CaraML describes its platform as helping data science teams run solutions at scale without engineering expertise.checked 4 Oct 2026
- Offline data
- It makes offline data available for real-time predictions without requiring custom pipelines.checked 8 Oct 2026
- Open source repository
- The maker links to a public GitHub repository described as the Feature Store for the CaraML Platform.checked 8 Oct 2026
- Purpose
- CARA ML Feature Store manages, stores, and serves machine learning features.checked 8 Oct 2026
- Repository components
- The repository includes modules for protobuf, PySpark, registry, SDK, serving, Spark, and test utilities.checked 8 Oct 2026
- Training and inference consistency
- It serves the same data to models during training and inference to address training-serving skew.checked 8 Oct 2026
- Web documentation
- The maker's product page links to documentation and a Get Started page.checked 8 Oct 2026
- Workflows
- It standardizes data engineering workflows across models and teams.checked 8 Oct 2026
Where it runs
- Platform licensing
- CaraML describes itself as an open source MLOps platform.checked 4 Oct 2026
