Machine Learning Service Job Queue Dependency Handling
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Solution Overview
Problem
Business organizations face barriers in utilizing machine learning due to the requirement of statistical and AI expertise, computational intensity, and challenges in handling large datasets, especially for infrequent but significant events like financial fraud detection, where traditional machine learning environments lack optimal mechanisms for data transformation and analysis.
Innovation Solution
A customizable machine learning service (MLS) with programmatic interfaces guides non-expert users through machine learning best practices, allowing default settings and enabling expert customization, using a provider network with distributed computing and storage for terabyte-scale data, and supporting asynchronous scheduling and secure data handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If machine learning models are developed using traditional methods requiring statistical and AI expertise, then model accuracy and performance can be achieved, but the complexity and difficulty of operation increase significantly for business analysts and subject matter experts
Solution Approach 1:
The patent introduces an automated machine learning service that acts as an intermediary between business analysts and complex ML algorithms. The service includes automated data preprocessing, feature engineering, model selection, and hyperparameter tuning components that translate business requirements into optimized ML models without requiring users to have statistical or AI expertise.
Solution Approach 2:
The system enables self-service machine learning by allowing business analysts to upload their data and specify their prediction goals, while the automated service independently performs data cleaning, transformation, model training, and evaluation. The system automatically selects appropriate algorithms and tunes parameters based on the data characteristics and problem type.
2Measurement precision
If machine learning techniques are applied to large datasets to improve prediction accuracy for infrequent events, then measurement precision improves, but the computational resources and time required increase significantly
Solution Approach 1:
The patent implements automated sampling and feature selection mechanisms that identify and focus computational resources on the most relevant data subsets and features for detecting infrequent events. The system automatically determines optimal sample sizes and feature combinations to achieve accurate predictions while minimizing unnecessary computational expenditure on redundant data processing.
Solution Approach 2:
The system automatically adjusts computational parameters such as data sampling rates, feature transformation methods, and model complexity based on the characteristics of the input data and the rarity of the target events. This dynamic parameter adjustment allows the system to optimize the balance between prediction accuracy and computational resource consumption for different datasets and problem types.
3Reliability
If data transformations are applied manually to prepare large datasets for machine learning, then data quality improves, but the time and effort required for data preprocessing increase significantly
Solution Approach 1:
The patent implements automated data preprocessing pipelines that perform data cleaning, normalization, transformation, and validation before the actual machine learning modeling begins. The system automatically detects data quality issues, applies appropriate cleaning transformations, and prepares features in advance, eliminating the need for manual data preparation and reducing preprocessing time significantly.
Solution Approach 2:
The system replaces manual mechanical data preprocessing operations with automated computational processes. Machine learning algorithms automatically perform feature engineering, data transformation, and quality assessment tasks that would otherwise require extensive manual effort, thereby improving data quality while reducing preprocessing time and human intervention.
Data Source
AI summary
A machine learning service implements programmatic interfaces for a variety of operations on several entity types, such as data sources, statistics, feature processing recipes, models, and aliases. A first request to perform an operation on an instance of a particular entity type is received, and a first job corresponding to the requested operation is inserted in a job queue. Prior to the completion of the first job, a second request to perform another operation is received, where the second operation depends on a result of the operation represented by the first job. A second job, indicating a dependency on the first job, is stored in the job queue. The second job is initiated when the first job completes.