Automated ML Data Pipeline Optimization via Cost-Based Transformation Refinement
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Solution Overview
Problem
Existing machine learning systems face inefficiencies in data preprocessing, which incurs significant computational costs and resources, often requiring substantial manual efforts and subjective optimizations, leading to bottlenecks in real-time and batch inference processes.
Innovation Solution
A method that receives a serialized pipeline, generates a data processing graph to identify transformations, determines computing costs, applies predefined optimizations such as increased parallelization or one-row optimizations, and replaces inefficient transformations with optimized ones, resulting in a refined pipeline with reduced computational expenses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual optimization methods are used to improve preprocessing efficiency, then some performance improvements can be achieved, but the process introduces additional costs, delays, and subjectivity
Solution Approach 1:
The system automatically analyzes and optimizes preprocessing pipelines without requiring manual intervention. The optimization process is self-executing, where the system identifies bottlenecks, evaluates alternative approaches, and implements improvements autonomously, eliminating the time loss and subjectivity associated with manual optimization methods
Solution Approach 2:
The system dynamically adjusts preprocessing parameters and configurations based on performance metrics and cost analysis. By automatically modifying transformation parameters, data sampling rates, and processing thresholds, the system achieves continuous optimization without manual intervention, resolving the contradiction between improvement quality and time investment
2Reliability
If comprehensive preprocessing is performed to ensure data quality, then model accuracy is improved, but computational cost and latency increase significantly
Solution Approach 1:
The system applies preprocessing transformations selectively rather than uniformly to all data. By identifying which transformations are actually needed for model accuracy and which can be skipped or simplified, the system performs partial preprocessing that maintains data quality while significantly reducing computational costs and energy consumption
Solution Approach 2:
The system performs preliminary analysis of the data and model requirements before executing the full preprocessing pipeline. By pre-identifying necessary transformations and filtering out unnecessary operations, the system ensures data quality is maintained while avoiding the computational overhead of comprehensive preprocessing
3Reliability
If preprocessing operations are increased to handle complex data, then model performance is improved, but the system becomes a bottleneck in real-time inference
Solution Approach 1:
The system segments the preprocessing pipeline into independent, parallelizable stages. By dividing complex preprocessing operations into separate transformation steps that can execute concurrently, the system maintains thorough data processing while reducing overall latency and preventing bottlenecks in real-time inference workflows
Solution Approach 2:
The system transitions from sequential preprocessing to parallel processing by adding a temporal dimension to the execution model. Multiple transformations are applied simultaneously across different data streams or batches, maintaining comprehensive processing capability while dramatically improving inference speed and eliminating system bottlenecks
Data Source
AI summary
Techniques for refinement of data pipelines are provided. An original file of serialized objects is received, and an original pipeline comprising a plurality of transformations is identified based on the original file. A first computing cost is determined for a first transformation of the plurality of transformations. The first transformation is modified using a predefined optimization, and a second cost of the modified first transformation is determined. Upon determining that the second cost is lower than the first cost, the first transformation is replaced, in the original pipeline, with the optimized first transformation.