Feature Management Platform for AI Model Prediction
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Solution Overview
Problem
Conventional methods for managing feature data in artificial intelligence and machine learning are resource-intensive, time-consuming, and lack governance, model integration, flexibility, reusability, and scalability, leading to inefficient data processing and timely generation of predictions.
Innovation Solution
A feature management platform that receives processing artifacts from computing devices, generates processing jobs to retrieve and transform event data, encapsulates feature values into vectors, and stores them in a feature store, allowing for real-time sharing and reusability across models, reducing dependency on data engineers and minimizing resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data processing methods are used, then data can be collected and stored, but resource consumption increases and time to generate predictions increases
Solution Approach 1:
The system pre-processes and stores feature data in a feature store before predictions are needed. Processing artifacts define how data should be transformed and prepared in advance, so when predictions are required, the data is already ready or can be quickly retrieved, eliminating the need for time-consuming real-time processing and reducing resource consumption during prediction generation.
Solution Approach 2:
The system creates reusable feature representations and processing artifacts that can be copied and shared across multiple models and computing devices. Instead of reprocessing the same data for each prediction, the system retrieves pre-computed feature vectors from the feature store, significantly reducing computational resource consumption and accelerating prediction generation.
2Productivity
If data processing is done manually by data engineers, then data can be managed, but time consumption increases and scalability is limited
Solution Approach 1:
The system enables self-service data processing by allowing computing devices to automatically define, execute, and manage data processing tasks through processing artifacts. The feature management platform automatically handles data retrieval, transformation, and storage without requiring manual intervention from data engineers, thereby increasing processing speed and eliminating scalability limitations imposed by human resources.
Solution Approach 2:
The system transforms manual data processing into automated parameter-driven operations. Processing artifacts encode all necessary processing parameters (data sources, transformations, storage locations), allowing the system to automatically adjust and optimize data processing based on changing requirements without human intervention, thus increasing productivity and reducing dependency on specialized personnel.
3Adaptability or versatility
If feature data is processed for each model independently, then data can be tailored to specific needs, but duplication occurs and reusability decreases
Solution Approach 1:
The system creates universal processing artifacts and feature representations that can serve multiple models and computing devices simultaneously. The feature store acts as a shared repository where data processed once can be reused by multiple models, eliminating duplication while maintaining the ability to tailor data to specific model needs through configurable processing artifacts.
Solution Approach 2:
The feature management platform acts as an intermediary between data sources and multiple models. It receives data from sources, applies transformations defined in processing artifacts, stores the processed data in the feature store, and makes it available to multiple models. This intermediary role enables data tailoring for specific models while preventing duplication through centralized storage and reuse.
4Speed
If real-time data processing is implemented, then predictions can be timely, but system complexity increases and reliability decreases
Solution Approach 1:
The system performs data processing and feature transformation in advance before predictions are needed. Processing artifacts are executed beforehand to pre-compute and store feature data in the feature store. This preliminary action ensures that when real-time predictions are required, the data is already prepared, maintaining speed while reducing the complexity and reliability risks associated with real-time processing operations.
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 feature the feature management platform can receive a processing artifact (e.g., a configuration file and code fragment) from a computing device. The processing artifact defines the 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, which when initiated generates a vector that encapsulates the feature data. The vector is transmitted to the computing device that locally hosts a model, which generates a prediction. The prediction is transmitted to the feature management platform and can be transmitted to other computing devices, upon request.