The entire feature lifecycle managed in one feature store
The Feature Store optimizes the entire feature lifecycle, allowing feature collaboration, ensuring consistency, and enhancing reliability in feature engineering and deployment.
Transform Your Data
Easily create features and build data pipelines with custom transformations across various data sources.
- Simplify feature creation to focus on the insights rather than infrastructure.
- Deploy data pipelines effortlessly, integrating multiple data sources to streamline your data workflow.
- Apply custom transformations to your data for full flexibility in your data processing needs.
Store Features
Large-scale and cost-effective offline store for training data and a lightning-fast, low-latency online store for inference data access during online serving.
Serve Features
Serve features both for model training and inference.
- Ensure low-latency access to features for real-time predictions and seamless integration into your production workflows.
- Automatically maintain feature consistency across environments
- Fill in missing values for high-quality data accuracy for robust model performance.
Data Ingestion
Ingest data from data warehouses and multiple sources.
Process, extract and transform relevant features, and store them in a feature store aggregate values.
Batch Feature Sets
Efficiently process and manage batch feature sets for periodic tasks such as customer segmentation reports, analyzing historical data, or processing large datasets in a scheduled manner. Ensure that batch features are consistently updated and available for model training and inference.
Streaming Feature Sets
Support real-time feature generation and processing with streaming data from Kafka. Continuously collect and process data as it is generated, enabling real-time analytics. Ideal for applications requiring real-time insights.
Feature Collaboration
Enable data scientists and ML engineers to easily collaborate and share features across projects.
Don’t just take our word for it
In the rapidly evolving landscape of property management technology, optimizing data processes remains paramount. Guesty, a leading player in this domain, faced challenges in streamlining its data science operations and hastening model deployment. This case study delves into Guesty's unique challenges and highlights how a strategic partnership with JFrog ML provided innovative solutions.