ML Data Processing Pipeline Scalability Optimization
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Solution Overview
Problem
Conventional data pre-processing methods in machine learning pipelines face performance bottlenecks when scaling from smaller to larger datasets, making it difficult to detect issues early and requiring additional hardware or code changes, which are not preventive solutions.
Innovation Solution
A method and system that uses sampling techniques to obtain performance parameters, predicts scalable performance for larger datasets, identifies anti-patterns, and recommends accelerated codes to address super-linear bottlenecks, thereby improving data processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data pre-processing operations are tested on smaller datasets, then functional correctness can be verified, but performance bottlenecks remain undetected
Solution Approach 1:
The system performs preliminary performance analysis on sampled data before full-scale processing. By analyzing performance characteristics on smaller sampled datasets and using curve fitting to predict scaling behavior, the system identifies potential bottlenecks early in the development cycle, preventing performance issues before they manifest on large datasets.
Solution Approach 2:
The system creates sampled copies of the actual dataset that preserve the statistical characteristics and data distribution. These sampled copies are then processed through the data processing code to measure performance parameters, providing a reliable proxy for predicting full-dataset performance without incurring the full computational cost.
2Productivity
If additional hardware is added to fix performance degradation, then processing capacity increases, but system complexity and cost increase
Solution Approach 1:
The system changes the parameters of the existing data processing code by identifying anti-patterns and recommending optimized code versions. This software-based optimization improves processing capacity without adding hardware, maintaining system simplicity while achieving better performance through code refinement.
Solution Approach 2:
The system automatically analyzes performance parameters, identifies bottlenecks, and recommends optimizations without requiring manual hardware additions. The self-service nature of the performance analysis and optimization recommendation process eliminates the need for complex hardware configurations and reduces system complexity.
3Productivity
If data processing code is optimized for larger datasets, then processing efficiency improves, but detection of bottlenecks becomes more difficult
Solution Approach 1:
The system performs preliminary performance measurement on sampled data to establish baseline performance parameters before optimization. By measuring performance on representative samples and using curve fitting to predict scaling behavior, the system identifies bottlenecks that would otherwise be invisible until full-scale processing is attempted.
Solution Approach 2:
The system uses sampled datasets as an intermediary between small test datasets and large production datasets. The sampling technique creates intermediate datasets that are large enough to reveal performance bottlenecks but small enough to allow rapid iteration and measurement, serving as a mediator that makes bottleneck detection feasible.
Data Source
AI summary
The present disclosure provides a scalable acceleration of data processing in Machine Learning pipeline which is unavailable in conventional methods. Initially, the system receives a dataset and a data processing code. A plurality of sample datasets are obtained based on the received dataset using a sampling technique. A plurality of performance parameters corresponding to each of the plurality of sample datasets are obtained based on the data processing code using a profiling technique. A plurality of scalable performance parameters corresponding to each of a plurality of larger datasets are predicted based on the plurality of performance parameters and the data processing code using a curve fitting technique. Simultaneously, a plurality of anti-patterns are located in the data processing code using a pattern matching technique. Finally, an accelerated code is recommended based on the plurality of anti-patterns and the predicted plurality of scalable performance parameters using an accelerated code recommendation technique.