The entire feature lifecycle managed in one JFrog ML Feature Store
The JFrog ML Feature Store optimizes the entire feature lifecycle, allowing feature collaboration, ensuring consistency, and enhancing reliability in feature engineering and deployment.
JFrog ML’s Feature Store provides effortless collaboration and feature sharing across projects. It serves as a single, discoverable source of truth for features used by production models.
Accelerate and simplify feature management no matter the size of the team or complexity of your data sets.
Transform Your Data
Easily create features to build and deploy data pipelines so you can focus on insights rather than infrastructure.
Manage the entire model lifecycle with a single centralized registry- Gain visibility into training parameters, hyper parameter tuning and model metadata
- Manage model and traditional software artifacts in one system
Store and Serve Features
Store, manage, and serve every feature in one system. Enable data scientists and ML engineers to easily collaborate and share features across projects.
- Large scale and cost-effective offline store for training data
- Lightning fast, low-latency online store for inference data and online serving
- 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.
Don’t be late in the game. AI apps need flexible infra, stay ahead or fall behind.
Book a DemoBatch 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.
Multi-Cloud: Our Cloud or Yours
JFrog ML offers native support for AWS and GCP, allowing you to deploy either on our platform or your own infrastructure for a streamlined flow.
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