Specialized Workers for Machine Learning Model Training
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Solution Overview
Problem
Conducting regression analysis and ranking content based on large datasets requires an undesirably large number of resources, as existing methods do not efficiently distribute and process positive and negative examples across workers in a distributed data processing system.
Innovation Solution
The system distributes positive and negative examples to specialized workers, generating weights for features by accumulating statistics across multiple instances, allowing for improved performance by optimizing resource usage through the MapReduce process and shard-based data organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all examples are distributed to all workers for processing, then comprehensive analysis is achieved, but resource consumption increases undesirably
Solution Approach 1:
The patent segments the dataset into positive examples and negative examples, and further segments workers into those specialized for positive examples and those for negative examples. This segmentation allows each worker to process only relevant examples, reducing resource consumption while maintaining comprehensive analysis coverage.
Solution Approach 2:
The patent applies local quality by creating workers with specialized configurations - some workers are optimized for processing positive examples while others are optimized for negative examples. Each worker has tailored processing logic and parameters suited to its specific example type, improving efficiency without sacrificing overall analysis quality.
2Quantity of substance
If workers process both positive and negative examples, then resource utilization is maximized, but processing efficiency decreases
Solution Approach 1:
The patent segments the worker population into specialized groups - workers dedicated to positive examples and workers dedicated to negative examples. This segmentation enables each worker to focus exclusively on its designated example type, eliminating the overhead of handling both types and thereby improving processing efficiency while maintaining effective resource utilization.
Solution Approach 2:
The patent changes the operational parameters of workers based on their specialization. Workers processing positive examples use parameters optimized for positive case characteristics, while workers processing negative examples use different optimized parameters. This parameter specialization improves processing efficiency for each worker type.
3Productivity
If specialized workers are used for positive and negative examples, then processing efficiency improves, but system complexity increases
Solution Approach 1:
While segmentation does increase structural complexity, the patent manages this by using straightforward segmentation criteria (positive vs. negative examples) and simple worker specialization rules. The segmentation is implemented through basic data filtering and worker assignment logic, keeping the added complexity manageable.
Solution Approach 2:
The patent maintains a form of universality by using a common worker framework that can be configured for different example types. The underlying worker architecture remains universal, with specialization achieved through configuration parameters rather than fundamentally different worker designs, thereby limiting the increase in system complexity.
Data Source
AI summary
Systems and techniques are disclosed for generating weighted machine learned models using multi-shard combiners. A learner in a machine learning system may receive labeled positive and negative examples and workers within the learner may be configured to receive either positive or negative examples. A positive and negative statistic may be calculated for a given feature and may either be applied separately in a model or may be combined to generate an overall statistic.