Inference Engine Data Modeling for Automated Pipeline Preparation
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Solution Overview
Problem
Existing automodeling systems are inefficient in data preparation, requiring significant time and effort, and impose restrictive constraints on data format compatibility between training and usage, necessitating additional coding and adapter development.
Innovation Solution
An innovative system that automates data preparation through an inspection-based approach, generating a probability distribution of potential pipelines, and utilizes a metadata file to ensure data format consistency across training and inference, reducing the need for separate coding and adapters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If inspection-based systems integrate domain expert knowledge to observe data and select transformations step-by-step, then model accuracy is improved, but time and effort required for data preparation increases significantly
Solution Approach 1:
The system performs preliminary actions by automatically generating a probability distribution of potential data transformation pipelines before model training begins. This pre-computation of transformation options eliminates the need for manual step-by-step inspection during actual model development, thereby maintaining accuracy while reducing time investment.
Solution Approach 2:
The system enables self-service by autonomously selecting appropriate transformations and pipelines without requiring domain experts to manually observe and evaluate each data transformation step. The automated pipeline selection process allows the system to serve itself, eliminating human time investment while preserving model accuracy through algorithmic domain knowledge integration.
2Adaptability or versatility
If search-based systems try a wide variety of transformations and models using probability distribution, then model versatility is improved, but computational resources and time required increases
Solution Approach 1:
The system applies partial action by generating a probability distribution that ranks transformation pipelines without exhaustively evaluating every possible transformation combination. By focusing computational resources on the most promising pipelines according to their probability scores, the system achieves sufficient model versatility without the excessive computational cost of complete search.
Solution Approach 2:
The system changes parameters by representing transformation pipelines as probabilistic distributions rather than fixed sequences. This parameter transformation allows the system to explore model versatility through probability weighting and sampling, achieving adaptability with improved computational efficiency compared to exhaustive search methods.
3Reliability
If separate coding and adapters are developed to ensure data format compatibility between training and usage, then data format consistency is improved, but device complexity and development effort increases
Solution Approach 1:
The system achieves universality by using the same probability distribution of transformation pipelines for both training and inference stages. This single unified mechanism handles data format compatibility across different stages without requiring separate coding or adapter development, thereby maintaining consistency while reducing overall system complexity.
Solution Approach 2:
The system merges the functions of training-time and inference-time data transformation into a single probability distribution model. By combining what would traditionally require separate coding efforts into one unified transformation pipeline selection process, the system maintains data format consistency while eliminating the need for additional adapter code.
Data Source
AI summary
This document presents a system and method for drastically decreasing the time and effort to go from a trained model to one viable for use in production by This drastically decreases time and effort to go from a trained model to one viable for use in production. The result of these innovations is that model creation time is now largely bound by training time and not data prep and coding for publication. The system provides data-observation-based inspections that yield a probability distribution to use for pipeline search in model creation. The result is that model creation time is now largely bound by training time and not data prep and coding for publication.