A centralized platform for managing, storing, and serving features for ML models.
This concept is essential for understanding practical deployment and forms a key part of modern AI systems.
Related Concepts
- MLOps
- Feature Engineering
- Data Pipeline
Reference 7 stops to get here
Feature Store
A centralized platform for managing, storing, and serving features for ML models.
Your route here
7 stops · basics first
- Dataset ✓ understood
A collection of data examples used for training, validating, or testing machine learning models.
- Feature ✓ understood
A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.
- Feature Engineering ✓ understood
The process of selecting, transforming, and creating input features to improve model performance.
- Training ✓ understood
The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.
- Inference ✓ understood
Running a trained model on new inputs to get predictions, with its weights frozen: the stage of a model's life that users actually interact with.
- Model Serving ✓ understood
Deploying trained models as services that can handle prediction requests in production environments.
- MLOps ✓ understood
Practices for deploying, monitoring, and maintaining machine learning models in production, combining ML and DevOps principles.
- Feature Store · you are here ✓ understood
Where it sits
Feature Store
Leads to
Nothing yet: a destination in its own right.
Explore nearby
Shipping AI Data Versioning Tracking different versions of datasets to ensure reproducibility and manage changes. Shipping AI Model Serving Deploying trained models as services that can handle prediction requests in production environments. Foundations Data Preprocessing Cleaning, transforming, and preparing raw data for model training (handling missing values, normalization, encoding).