Energy Data Science Platform for Automated Model Deployment
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Solution Overview
Problem
The energy industry faces challenges in developing an integrated platform that efficiently automates and streamlines workflows by reducing time-consuming and monotonous human-intensive tasks using energy data, which often relies on indirect measurements and human experience.
Innovation Solution
An integrated energy data science platform is implemented, incorporating a data discovery component, model selection component, and model deployment mechanism, enabling automated data visualization, machine learning model development, and deployment, with features like map views, model selection, and deployment settings, allowing users to access, train, and serve machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data processing and analysis methods are used in the energy industry, then human expertise and experience can be applied to interpret indirect measurements, but the process becomes time-consuming and monotonous
Solution Approach 1:
The system enables automated self-service processing where the platform automatically discovers data, trains machine learning models, and generates insights without requiring continuous human intervention. The automated workflow includes data discovery, model training, and result generation that operates independently once initiated.
Solution Approach 2:
Manual mechanical processes of data processing and analysis are replaced with automated computational systems. The platform uses machine learning models and algorithms to substitute human analysts in interpreting energy data, transforming the mechanical human-analyst process into an automated computational system.
2Productivity
If an integrated automated platform is implemented to reduce manual tasks, then workflow efficiency and automation are improved, but the system complexity increases
Solution Approach 1:
The platform is designed as a universal system that performs multiple functions including data discovery, model training, model deployment, and result visualization. This multi-functional approach consolidates what would otherwise require multiple separate systems into a single integrated platform, managing complexity through consolidation.
Solution Approach 2:
The platform is divided into distinct modular components: data discovery component, model training component, model deployment component, and visualization component. Each component handles specific tasks independently, allowing the system to manage complexity through modular architecture while maintaining integration benefits.
3Extent of automation
If machine learning models are deployed to automate data analysis, then human intervention is reduced and efficiency increases, but access control and authorization requirements become more complex
Solution Approach 1:
The platform introduces an intermediary authorization layer that manages access control between users and machine learning models. This intermediary component handles authentication and permission verification, shielding the complex automation processes from direct user interaction while maintaining security and access control.
Data Source
AI summary
A method implements an integrated energy data science platform. The method includes presenting a data discovery component, including a map view to select data using the map view. The method further includes presenting a model selection component to select a machine learning model configured with deployment settings and configured to use the data, wherein the deployment settings identify sample features of the data. The method further includes authorizing access to the machine learning model and the data, deploying the machine learning model using the deployment settings, and presenting results generated from the sample features using the machine learning model, wherein the sample features are extracted from the data.