Multimodal Model Composition for Complex Data Sources
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Solution Overview
Problem
Creating a machine learning model for complex data sources, such as those involving multiple types of data like images, sensor data, and traditional numerical data, is challenging due to the difficulty in combining different data sources and domains, which limits the efficiency of AutoAI tools when dealing with diverse data types.
Innovation Solution
A system that composes a new model architecture by combining two or more existing models based on the problem domain and data types, using a model composer program that receives input data and metadata, determines the task context, evaluates matches with pre-compiled models, selects appropriate models, generates multimodal model combinations, transforms data features, and executes these combinations to provide user-acceptable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional AutoAI tools are used for complex data sources, then the model building process becomes difficult and time-consuming, but using multiple existing models for each data type increases complexity and integration difficulty
Solution Approach 1:
The system segments the complex modeling task by identifying different data types (images, sensor data, numerical data) and assigning specialized pre-compiled models to each type. This segmentation allows each model to handle specific data types efficiently while the composition system integrates them through a standardized interface, resolving the contradiction between handling complex data sources and maintaining model building efficiency.
Solution Approach 2:
The system creates a universal model composition framework that can handle multiple data types and model types through a common interface. The composition system acts as a multi-functional platform that orchestrates different specialized models (image processing models, sensor data models, numerical data models) to solve diverse machine learning problems, thereby improving productivity without proportionally increasing complexity.
2Measurement precision
If a single model is used for all data types, then model simplicity is maintained, but accuracy and efficiency decrease when dealing with diverse data types
Solution Approach 1:
The system applies local quality by selecting and applying different specialized models for different data types within the same machine learning task. For example, convolutional neural networks are used for image data where they provide superior accuracy, while different model architectures are used for sensor data and numerical data. This localized optimization of model selection for each data type improves overall prediction accuracy without requiring a completely complex custom architecture, as the complexity is managed through automated model composition.
Data Source
AI summary
In an approach to composing a machine learning model for complex data sources, a computer receives data and associated metadata corresponding to a machine learning task from a user. A computer determines a task context and a problem domain. A computer identifies the machine learning task. A computer evaluates a match between the problem domain and one or more pre-compiled models. A computer selects at least two of the one or more pre-compiled models. A computer generates one or more multimodal model combinations with the selected at least two of the one or more pre-compiled models. A computer executes the multimodal model combinations with the data and associated metadata. A computer displays the results of the executed one or more multimodal model combinations to the user. A computer determines whether a level of error associated with the results is acceptable to the user based on a response from the user.