We present how Hopsworks leverages its time-travel capabilities for feature groups to support reproducible creation of training datasets using metadata.
Learn more about how Hopsworks (RonDB) outperforms AWS Sagemaker and GCP Vertex in latency for real-time AI databases, based on a peer-reviewed SIGMOD 2024 benchmark.
Read how Hopsworks generates temporal queries from Python code, and how a native query engine built on Arrow can massively outperform JDBC/ODBC APIs.
In this article, we cover the added value of a feature store over a data warehouse when managing offline data for AI.
In this article we introduce the snowflake schema data model for feature stores, and show how it helps you include more features to make better predictions
This article introduces a taxonomy for data transformations in AI applications that is fundamental for any AI system that wants to reuse feature data in more than one model.
We present a unified software architecture for batch, real-time, and LLM AI systems that is based on a shared storage layer and a decomposition of machine learning pipelines.
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