Automated Data Science Service for Real-Time Prediction
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Solution Overview
Problem
Manual analysis of datasets in organizations is time-consuming, inaccurate, and laborious, hindering IT teams' ability to respond promptly to issues.
Innovation Solution
A Data Science as a Service (DSaaS) system that receives training data, performs high-level analysis, identifies essential variables, trains machine learning engines, and applies production data to output predictions in real-time, using models like Naïve Bayes, XGBoost, or Logistic Regression, and returns insights into data types, distributions, and classifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual analysis of datasets is performed, then labor-intensive steps are executed, but analysis time is excessive and accuracy is reduced
Solution Approach 1:
The patent replaces manual mechanical analysis processes with automated machine learning systems. The DSaaS platform uses trained machine learning models to automatically analyze datasets, substituting human analysts' manual work with computational algorithms that process data much faster and with consistent accuracy.
Solution Approach 2:
The system enables self-service through automated data analysis pipelines. The machine learning models independently process datasets without requiring manual intervention at each step, automatically performing feature engineering, model training, and prediction generation, thereby eliminating the need for continuous human oversight and significantly reducing processing time.
2Measurement precision
If manual data analysis processes are used, then repetitive steps are performed, but accuracy is compromised
Solution Approach 1:
The patent segments the complex data analysis process into distinct automated components: data preprocessing, feature selection, model training, validation, and deployment. Each segment is handled by specialized machine learning modules within the DSaaS platform, maintaining high accuracy while managing complexity through modular automation rather than manual execution.
Solution Approach 2:
The system automatically optimizes analysis parameters through machine learning model training and hyperparameter tuning. The DSaaS platform adjusts model parameters, feature weights, and processing thresholds based on the specific characteristics of each dataset, thereby maintaining high accuracy across diverse data types without requiring manual parameter adjustment for each case.
3Productivity
If automated machine learning systems are implemented, then analysis time is reduced, but system complexity increases
Solution Approach 1:
The DSaaS platform implements a universal machine learning system that handles multiple data types, analysis tasks, and model types through a single integrated architecture. The platform provides standardized interfaces and workflows that work across different datasets and problems, reducing the need for multiple specialized systems and managing complexity through consolidation rather than proliferation of separate tools.
Solution Approach 2:
The patent introduces an intermediary DSaaS platform layer between raw data and final insights. This intermediary automatically manages the complexity of machine learning operations by providing pre-built models, automated feature engineering, and standardized deployment pipelines, thereby shielding end users from underlying system complexity while maintaining high processing throughput.
Data Source
AI summary
A method for providing data science as a service may include a computer program: receiving training data; receiving a type of machine learning engine to train; performing a high-level data analysis on the training data; returning a plurality of essential variables to perform prediction in order of importance; receiving a selection of one or more of the essential variables; training the machine learning engine with the training data using the type of machine learning engine to train and the selected one or more essential variables; receiving production data from one or more production systems; applying the production data to the trained machine learning engine; and outputting an output of the trained machine learning engine to the one or more production systems and/or a data consumer, wherein the one or more production systems and/or the data consumer is configured to consume the output of the trained machine learning engine.