Dynamic Machine Learning Model Interface via Metadata
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Solution Overview
Problem
Current solutions for deploying machine learning models as services lack flexibility in discovering model details, such as features and linked data sources at runtime, leading to rigid user interfaces and duplicated effort in feature engineering and model maintenance.
Innovation Solution
A platform with a repository for storing trained machine learning models and their metadata, allowing dynamic linking of data sources and real-time feature engineering during model training, enabling flexible model interaction and reducing duplication of logic across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning models are deployed as services with static feature engineering, then model training is simplified, but flexibility in discovering model details at runtime is lost and user interfaces must be custom developed
Solution Approach 1:
The system dynamically generates user interfaces at runtime based on model metadata and linked data sources, rather than using static pre-defined interfaces. This allows the interface to adapt automatically to different models and their features, resolving the contradiction between simplified model training and runtime flexibility.
Solution Approach 2:
The system automatically discovers model details, features, and linked data sources through metadata stored with each model. This self-service capability eliminates the need for manual custom development of user interfaces and application integrations, while maintaining full flexibility at runtime.
2Measurement precision
If feature engineering is performed statically on training data sets, then model training accuracy is improved, but duplicated logic must be maintained across multiple systems
Solution Approach 1:
The system merges feature engineering logic into a centralized feature store that is shared across all models and consuming applications. This eliminates duplicated logic across multiple systems while maintaining the accuracy benefits of static feature engineering, as the same feature transformation logic is reused consistently.
Solution Approach 2:
The feature store creates universal feature vectors that can be used by multiple models and applications simultaneously. This multi-functional approach allows a single feature engineering implementation to serve multiple purposes, reducing system complexity while preserving training accuracy.
3Adaptability or versatility
If data sources are linked to models with metadata collection, then model interaction flexibility is enhanced, but additional metadata management overhead is introduced
Solution Approach 1:
The system introduces a metadata layer that acts as an intermediary between data sources and models. This metadata automatically captures essential information about linked data sources and model features, enabling flexible model interaction without requiring complex custom integration logic for each model-data source pair.
Data Source
AI summary
A system includes a repository storing trained machine learning models and metadata corresponding to the trained machine learning models. The system provides an interface for performing operations on the trained machine learning models and corresponding metadata stored in the repository, and evaluates trained machine learning models that are linked to the data sources and stored in the repository to obtain resulting predictions for a payload of records of data contained in the data sources. A method includes configuring one or more data sources, ingesting training data from the configured data sources, identifying a scenario for prediction, training one or more machine learning models on the ingested training data for the identified scenario, reviewing performance of the trained machine learning models and storing the trained machine learning models and corresponding metadata in A model repository, and dynamically creating a user interface for interacting with the stored trained machine learning models.