Differential AI Model Storage for Reproducible Data Transformations
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Solution Overview
Problem
Existing storage systems in artificial intelligence infrastructure face challenges in efficiently managing and optimizing machine learning model transformations and dataset storage, leading to inefficiencies and lack of reproducibility.
Innovation Solution
Implementing a system that stores information about dataset transformations and previous versions of machine learning models within storage systems, allowing these to be used as inputs for model executions, and utilizing non-volatile solid state storage units for quick data access and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional storage systems are used for machine learning model data, then storage capacity is sufficient, but data access speed and transformation efficiency are slow
Solution Approach 1:
The storage system is segmented into multiple storage units with different performance characteristics. High-speed storage units (e.g., SSDs, memory) are used for frequently accessed model data and transformation intermediates, while lower-speed units are used for archival storage. This segmentation allows critical operations to access data quickly without requiring all storage to be high-speed.
Solution Approach 2:
The system performs preliminary actions by pre-processing and transforming datasets before they are needed for model training. Transformation pipelines prepare data in advance and store intermediate results in optimized formats, reducing the transformation time during actual model execution. Data is pre-loaded into high-speed storage buffers before predicted access needs.
2Reliability
If detailed transformation information is stored for reproducibility, then reproducibility improves, but storage complexity and overhead increase
Solution Approach 1:
Instead of storing complete transformation pipelines and processing logic, the system stores simplified copies or representations of transformation states. These include configuration parameters, data provenance metadata, and transformation signatures that can reproduce the original transformations without requiring the full transformation engine to be preserved. This reduces storage complexity while maintaining reproducibility.
Solution Approach 2:
The storage system uses a nested structure where transformation information is organized in hierarchical layers. Core transformation parameters are stored at higher levels with broader scope, while detailed implementation specifics are nested at lower levels only when needed. This nested organization allows the system to maintain reproducibility through layered information without requiring all details to be simultaneously accessible, reducing overall complexity.
3Adaptability or versatility
If multiple versions of machine learning models are stored, then model iteration and comparison are improved, but storage space consumption increases
Solution Approach 1:
The system merges common elements across multiple model versions into shared storage structures. Identical or similar transformation pipelines, data preprocessing steps, and configuration parameters are consolidated into single stored representations that can be referenced by multiple model versions. This combining reduces redundant storage while preserving the ability to reconstruct any specific model version.
Solution Approach 2:
Instead of storing complete copies of each model version, the system stores parameterized representations where model variations are captured through parameter changes rather than full data duplication. Transformation configurations use parameter references that can be modified to generate different model versions, reducing storage requirements while maintaining adaptability for model iteration and comparison.
Data Source
AI summary
Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.