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The Definitive Guide to Feature Stores in 2024
As the landscape of machine learning (ML) evolves, the demands on ML teams for building robust ML systems have become increasingly intricate. A Data/ML engineer needs to, more than ever, rely on their skillset as well as their MLOps toolbox in order to deliver versatile capabilities and complex ML Systems. Feature Stores are proving to be one of these invaluable tools, offering multifaceted assistance to ML teams.
A Feature Store is a data platform that supports the development and operation of machine learning systems by managing the storage and efficient querying of feature data. Machine learning systems can be real-time, batch or stream processing systems, and the Feature Store is a general purpose data platform that supports a multitude of write and read workloads.
Here is a definitive roadmap to the state of Feature Stores in 2024 and the solutions they provide for ML teams with real-world problems. Learn how Feature Stores solve challenges for deploying models to production, scaling the number of model deployments as well as scaling the size of your ML team through:
- Collaborative Development
- Incremental Datasets
- Backfill feature data and training data
- Point-in-Time Consistent Training Data
- Feature Reuse
- Feature validation/monitoring
- Similarity search
Delve into detailed insights on these aspects and uncover the ways in which Feature Stores can assist with common ML deployment scenarios. Gain a comprehensive understanding of how these tools contribute to the success of ML teams in 2024.
Read the Guide to Feature Stores
Feature Store Benchmarks: Hopsworks vs Feast
With a number of feature stores on the market it can be hard to differentiate which Feature Store to choose as it depends on the specific requirements and preferences of the user and the application.
We put together an article, comparing the performance of online feature serving for Hopsworks and Feast, contrasting two different approaches to building a feature store - Hopsworks includes its own online store, RonDB, while Feast provides a pluggable online store.
Read to find out which Feature Store performed with much lower read latency for online feature vector retrieval.
