TL;DR:
- Featurestore.org introduces groundbreaking feature store benchmarks at Feature Store Summit 2023.
- Benchmarks were created through collaboration with Hopsworks, Karolinska Institute, and KTH University.
- Feature Stores are vital for structured data management in machine learning.
- Benchmarks cover Offline API throughput, Online API latency, and Feature Freshness.
- Jim Dowling, CEO of Hopsworks, emphasizes the importance of benchmarks for performance evaluation.
- Marks a significant step towards measuring Feature Store performance in real-world applications.
- Initiates a community effort to build a comprehensive suite of benchmarks for Feature Stores.
Main AI News:
In a groundbreaking announcement at the Feature Store Summit 2023, featurestore.org, a distinguished forum that unites the global community of Feature Store platform users and developers, proudly introduced a set of new, industry-defining feature store benchmarks. These benchmarks are the result of a fruitful collaboration between Hopsworks, Karolinska Institute, and KTH University.
The realm of machine learning has witnessed a burgeoning need for Feature Stores—data platforms specifically tailored to support the development and operation of machine learning systems. While recent benchmarks for AI systems, such as TPCx-AI, have emerged, they encompass an extensive range of use cases, including the processing of video and images. In contrast, Feature Stores are meticulously designed to excel in managing structured data emanating from databases, data warehouses, and various file sources.
Within this framework, the Feature Store community has undertaken the development of an inaugural set of benchmarks meticulously crafted to address common usage patterns of Feature Stores. Presently, three benchmarks have been unveiled:
- Offline API Benchmark: This benchmark gauges the throughput of a Feature Store for creating training data in the form of Pandas DataFrames or files.
- Online API Benchmark: Designed to measure the latency of online feature serving AI-powered applications, this benchmark plays a pivotal role in assessing real-time performance.
- Feature Freshness Benchmark: Highlighting the crucial metric of data freshness, this benchmark measures the time taken for a computed feature to become accessible in the online feature store for immediate serving.
Jim Dowling, the esteemed CEO of Hopsworks, remarked, “We are delighted to present these benchmarks to the Feature Store community, enabling users to effortlessly validate the performance claims made by vendors. As a Feature Store community, the need for benchmarks is imperative to gauge the progress within our field. The benchmarks presented adhere to the rigorous principles of database benchmarking, ensuring reproducibility, fairness, and the incorporation of realistic workloads.“
The introduction of these benchmarks signifies a significant leap forward in the quest to empower Feature Store users with the means to measure and comprehend the real-world performance of Feature Stores. It is essential to emphasize that these benchmarks represent just the initial stride in an ongoing community initiative aimed at creating a comprehensive suite of benchmarks tailored specifically for Feature Stores, fostering innovation and driving excellence within the industry.
Conclusion:
These new feature store benchmarks represent a pivotal development in the field of machine learning and data management. They provide a standardized and rigorous way to evaluate the performance of Feature Stores, fostering transparency and competitiveness in the market. With these benchmarks, users can make informed decisions when selecting Feature Store solutions, ultimately driving innovation and excellence in the industry.
