Machine Learning Transform Metadata for Consistent Prediction Preprocessing
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Solution Overview
Problem
Existing machine learning systems face issues such as training prediction skew and lack of predefined data preprocessing functions, leading to inefficiencies and inaccuracies in model training and prediction processes.
Innovation Solution
Implementing a method that determines whether a transform is specified in the instruction, generates transformed training data using statistics, and stores the transform and statistics as metadata for the model, allowing for seamless data preprocessing within SQL engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually preprocess data with SQL expressions before training, then data can be transformed, but training prediction skew occurs and users must know all transform parameters
Solution Approach 1:
The system automatically captures transform parameters and statistics during model training and stores them as metadata. During prediction, the system automatically applies the same transforms using the stored metadata, eliminating the need for users to manually specify transform parameters and ensuring consistency between training and prediction preprocessing.
Solution Approach 2:
The system performs transform parameter capture and statistics calculation during the training phase before prediction occurs. By preliminarily computing and storing all necessary transform parameters (min, max, mean, std) as metadata during training, the system prepares everything needed for consistent prediction preprocessing without requiring user intervention later.
2Reliability
If users specify transforms during training, then data can be transformed, but users must also specify the same transforms during prediction which is cumbersome
Solution Approach 1:
The system automatically manages transform specifications by capturing them during training and storing as metadata. During prediction, the system automatically retrieves and applies the stored transform specifications without requiring users to re-specify them, maintaining transform consistency while simplifying operations.
Solution Approach 2:
The system creates a copy of the transform specifications from the training phase and stores them as metadata with the model. During prediction, this copied transform information is automatically applied, ensuring that the exact same transforms used during training are replicated during prediction without manual user specification.
3Adaptability or versatility
If SQL engine lacks predefined preprocessing functions, then flexibility is maintained, but common feature engineering becomes difficult
Solution Approach 1:
The system provides a universal metadata storage mechanism that can accommodate any type of transform parameter and statistics. This universal approach maintains flexibility for custom transforms while also supporting common feature engineering operations through standardized metadata fields for min, max, mean, std, and other statistical parameters.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for transformation for machine learning pre-processing. In some implementations, an instruction to create a model is obtained. A determination is made whether the instruction specifies a transform. In response to determining that the instruction specifies a transform, a determination is made as to whether the transform requires statistics on the training data. The training data is accessed. In response to determining that the transform requires statistics on the training data, transformed training data is generated from both the training data and the statistics. A model is generated with the transformed training data. A representation of the transform and the statistics is stored as metadata for the model.