Remote Data Transfer for Machine Learning via Virtual Annotations
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Solution Overview
Problem
Current systems face challenges in efficiently transferring data between computing systems, particularly for machine learning analyses, due to the complexity of identifying and manually configuring data transfer processes, which requires multiple skill sets and increases time, cost, and complexity, often leading to forgone opportunities for data analysis.
Innovation Solution
The solution involves using annotations in a virtual data model to specify structured data transfer and unstructured data processing, with features like change detection protocols and integration scenarios, facilitating the transfer of data between systems for machine learning tasks without the need for extensive technical expertise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is transferred from a local computing system to a remote computing system for machine learning analysis, then machine learning capabilities are enhanced and data availability is increased, but the complexity of identifying and configuring data transfer processes increases, requiring multiple skill sets and increasing time and cost
Solution Approach 1:
The patent introduces an intermediary system that automatically identifies and configures data transfer processes between local and remote computing systems. This intermediary component resolves the technical contradiction by handling the complex configuration tasks automatically, thereby enhancing machine learning capabilities through remote data access while eliminating the need for manual configuration by multiple skilled personnel.
2Reliability
If manual configuration of data transfer processes is used, then data can be transferred between systems, but the process requires multiple skill sets and increases time and cost
Solution Approach 1:
The patent implements a self-service mechanism where the system automatically performs data transfer configuration without requiring manual intervention. The system autonomously identifies data sources, determines transfer requirements, and configures the transfer processes, thereby maintaining reliable data transfer while eliminating the time loss associated with manual configuration by multiple skilled personnel.
3Manufacturing precision
If extensive technical expertise is required for data transfer configuration, then accurate data transfer can be achieved, but the complexity and cost increase
Solution Approach 1:
The patent employs an intermediary system that acts as a bridge between users and the complex data transfer configuration processes. This intermediary automatically handles the technical aspects of data transfer setup, ensuring accurate and reliable data transfer between systems while making the process easy to operate for users regardless of their technical expertise level.
Data Source
AI summary
Techniques and solutions are described for facilitating the use of machine learning techniques. In some cases, a system suitable for providing a machine learning analysis can be different from a remote computer system on which training data for a machine learning model is located. A machine learning task can be defined that includes an identifier for at least one data source on the remote computer system. Data for the at least one data source is received from the remote computer system. At least a portion of the data is processed using a machine learning algorithm to provide a trained model, which can be stored for later use. Data on the remote computing system can be unstructured or structured. Particularly in the case of structured data, a remote computer system can make updated data available to the machine learning task.