Shared Cloud ML Platform for Enterprise Prediction Accuracy
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Solution Overview
Problem
Enterprises face challenges in evaluating data stored in cloud-based networks due to the high computational resource consumption and cost associated with machine learning (ML) software, which can be costly and resource-intensive.
Innovation Solution
A cloud-based network system that provides a remote ML arrangement, allowing enterprises to securely generate and access ML models and predictions based on their data without the need for significant computational resources or costly specialized software, by utilizing a computing system and trainer devices to generate and execute ML models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If enterprises use machine learning software to evaluate cloud-based data, then prediction accuracy and operational decision-making improve, but computational resource consumption and costs increase significantly
Solution Approach 1:
The patent combines multiple enterprise instances' ML training needs into a single shared cloud-based ML platform. Multiple enterprises share the same computational infrastructure and ML trainer processes, merging their resources to achieve accurate predictions while reducing individual computational burdens and costs.
Solution Approach 2:
The cloud-based ML platform provides universal ML training capabilities that serve multiple different enterprise instances through a single system. The platform can handle various ML training requests from different enterprises using shared computational resources, making the system multi-functional and resource-efficient.
2Adaptability or versatility
If enterprises deploy local ML trainer processes, then they maintain full control over data and modeling, but the complexity of managing and updating ML trainers across multiple instances increases
Solution Approach 1:
The patent extracts the complex ML trainer management functionality from individual enterprise instances and consolidates it into a centralized cloud-based platform. This removes the burden of managing, updating, and maintaining ML trainers across multiple instances, while still allowing enterprises to control their own data and modeling parameters through secure cloud access.
Solution Approach 2:
The cloud-based ML platform acts as an intermediary between enterprises and the complex ML training infrastructure. It mediates between enterprise data requirements and the computational processes needed for ML training, simplifying the interface while maintaining enterprise control over their specific modeling needs.
3Productivity
If enterprises use specialized ML software, then they gain access to advanced prediction capabilities, but the cost of obtaining and maintaining this software increases
Solution Approach 1:
The patent creates a shared cloud-based copy of ML training capabilities that can be accessed by multiple enterprises simultaneously. Instead of each enterprise purchasing and maintaining separate specialized ML software, they access a centralized platform that provides the same advanced prediction capabilities through shared infrastructure, significantly reducing individual software costs.
Data Source
AI summary
A network system may include a plurality of trainer devices and a computing system disposed within a remote network management platform. The computing system may be configured to: receive, from a client device of a managed network, information indicating (i) training data that is to be used as basis for generating a machine learning (ML) model and (ii) a target variable to be predicted using the ML model; transmit an ML training request for reception by one of the plurality of trainer devices; provide the training data to a particular trainer device executing a particular ML trainer process that is serving the ML training request; receive, from the particular trainer device, the ML model that is generated based on the provided training data and according to the particular ML trainer process; predict the target variable using the ML model; and transmit, to the client device, information indicating the target variable.