Stateful Featurization Platform for Real-Time ML Inference
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Solution Overview
Problem
Conventional methods for managing feature data in artificial intelligence and machine learning models are resource-intensive, time-consuming, and lack governance, model integration, flexibility, and scalability, leading to inefficient data processing and delayed predictions.
Innovation Solution
A feature management platform that receives processing artifacts to generate and manage stateful features by retrieving event data, applying transforms, aggregating values, and providing feature vectors to computing devices, reducing dependency on data engineers and enabling real-time data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data processing methods are used to manage feature data, then feature data can be generated and stored, but resource consumption increases and processing time is extended
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing feature data in a feature store before it is needed for model training or inference. Feature engineering transformations are applied in advance to raw data, and computed features are cached for rapid retrieval. This eliminates the need to re-compute features during critical inference operations, significantly reducing processing time and improving productivity.
Solution Approach 2:
The patent introduces a feature store as an intermediary component between raw data sources and machine learning models. This feature store acts as a mediator that stores pre-computed feature data, allowing models to quickly access processed features without directly querying raw data sources or triggering complex data engineering pipelines. The intermediary feature store decouples feature computation from model execution, improving overall system efficiency.
2Extent of automation
If conventional data processing methods are used, then feature data can be managed, but dependency on data engineers increases and automation decreases
Solution Approach 1:
The system implements self-service capabilities by enabling automated feature engineering and management without requiring manual intervention from data engineers. The feature store automatically discovers data sources, applies predefined transformations, computes features, and manages caching policies. Models can autonomously retrieve features from the feature store using simple APIs, eliminating the need for data engineers to manually prepare and manage feature data for each model or experiment.
Solution Approach 2:
The feature store is designed as a universal system that serves multiple functions: storing raw data, caching computed features, managing feature engineering transformations, and providing standardized access interfaces. This multi-functional platform consolidates what would otherwise require separate systems for data storage, feature engineering, and model serving, reducing overall system complexity while increasing automation capabilities.
3Adaptability or versatility
If conventional data processing methods are used, then feature data can be generated, but scalability and reusability are limited
Solution Approach 1:
The system merges feature data from multiple sources and models into a unified feature store. Computed features are cached and made reusable across different models, experiments, and applications. Instead of creating separate feature pipelines for each model, the system consolidates feature computation and storage, allowing any model to benefit from pre-computed features. This sharing mechanism increases adaptability and reusability while reducing the total quantity of feature data that needs to be created and maintained.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques for operation of a feature management platform. A feature management platform is an end-to-end platform developed to manage the full lifecycle of data features. For example, to create a stateful feature, the feature management platform can receive a processing artifact from a computing device. The processing artifact defines the stateful feature, including the data source to retrieve event data from, when to retrieve the event data, the type of transform to apply, etc. Based on the processing artifact, the feature management system generates a processing job (e.g., the API defines a pipeline), which when initiated generates a vector that encapsulates the stateful feature. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction that is transmitted to the feature management platform. Subsequently, the predication and stateful feature can be transmitted to other computing devices.