Embedding Serialized Machine Learning Models in Documentation Files
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Solution Overview
Problem
Machine learning models (MLMs) face challenges in portability and deployment due to disconnected information regarding their usage, requiring specific machine learning platforms for prediction tasks, which limits their reuse and indexing in catalogs.
Innovation Solution
Embedding MLMs in a serialized format into documentation files after a delimiter indicator, allowing for storage and retrieval while maintaining documentation and usage information, enabling seamless deployment across different platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If machine learning models are stored separately from documentation, then model storage is simplified, but usage information becomes disconnected and portability is limited
Solution Approach 1:
The patent combines the machine learning model and its documentation into a single file by embedding the serialized model data within the documentation file structure. This merging ensures that usage information remains connected to the model, improving portability and reuse while maintaining storage simplicity through a unified file format.
Solution Approach 2:
The patent embeds the serialized machine learning model data inside the documentation file, creating a nested structure where the model is contained within the file that also holds its documentation. This nesting preserves both the model and its usage information together, enabling seamless portability across different platforms without requiring separate storage mechanisms.
2Loss of information
If machine learning models are embedded in documentation files, then usage information is maintained, but file structure complexity increases
Solution Approach 1:
The patent introduces a delimiter indicator as an intermediary element that marks the boundary between documentation content and embedded model data within the file. This delimiter simplifies the file structure by providing a clear separation point, making it easier to parse and extract the model without requiring complex file structure management.
Solution Approach 2:
The patent utilizes serialization to transform the machine learning model into a compact data format that can be embedded within the documentation file. This parameter change in the model's representation reduces the complexity of managing embedded data while preserving all necessary usage information and model functionality.
3Reliability
If machine learning models require specific platforms for deployment, then model accuracy is maintained, but adaptability and reuse across different platforms is reduced
Solution Approach 1:
The patent creates a universal file format that can store and transport machine learning models along with their documentation in a platform-independent manner. The embedded serialized model and its associated usage information can be accessed and deployed across different platforms, enhancing adaptability and reuse while maintaining the model's predictive capabilities through proper serialization formats.
Data Source
AI summary
A processing system including at least one processor may obtain a machine learning model, serialize the machine learning model into a serialized format, and embed a delimiter indicator into a documentation file comprising information regarding the use of the machine learning model, where the delimiter indicator is in a file position that is after an end-of-file indicator of the documentation file. The processing system may further embed the machine learning model in the serialized format into the documentation file in a file position that is after the delimiter indicator. The processing system may then store the documentation file with the delimiter indicator and the machine learning model in the serialized format that are embedded.