Iguazio
Accelerate the path to production AI with a real-time MLOps orchestration platform.
The Open-Source Virtual Feature Store for Enterprise ML Governance and Orchestration.
Featureform is a leading virtual feature store that fundamentally redefines how ML features are managed by decoupling feature definitions from the underlying data infrastructure. Unlike traditional feature stores that require moving data into a proprietary database, Featureform acts as an orchestration and metadata layer that sits atop existing stacks like Snowflake, Spark, BigQuery, and Redis. As of 2026, it has become a critical component for organizations requiring high-compliance data lineage and point-in-time correctness without vendor lock-in. The platform allows data scientists to define features in Python, while the system handles the underlying transformations, registration, and serving. This architecture ensures that training-serving skew is eliminated by using the same logic across both environments. Its enterprise-grade governance capabilities provide RBAC and exhaustive lineage, making it indispensable for regulated industries like FinTech and Healthcare that must audit every piece of data used in predictive modeling. By 2026, Featureform is positioned as the standard for 'Virtual' feature management, optimizing compute costs by reusing existing infrastructure efficiently.
Orchestrates transformations on existing infrastructure (Spark/Snowflake) rather than copying data into a new silo.
Accelerate the path to production AI with a real-time MLOps orchestration platform.
The first open-source feature store for high-performance ML pipelines.
The Open-Source Orchestration Framework for Seamless MLOps Automation.
The open-source standard for consistent ML feature serving and storage across training and production.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Automatically handles time-travel joins to ensure training data perfectly reflects the state of the world at prediction time.
Provides a single interface to fetch features for both offline training and online low-latency inference.
Automatically generates a DAG showing the relationship between raw data, transformations, features, and models.
Enables sharing and discovery of features across different teams and projects through a centralized registry.
Write transformation logic in Python or SQL and switch underlying compute from Spark to BigQuery without rewriting code.
Every change to a feature definition is tracked via a versioning system similar to Git for data.
Latency issues when joining user transaction history with real-time events.
Registry Updated:2/7/2026
Models appearing highly accurate during training but failing in production due to 'future' data usage.
Different departments reinventing the same 'User_Engagement' feature multiple times.