ML Algorithm Recommendation Platform for Workspace Configuration
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Solution Overview
Problem
Current approaches for selecting machine learning algorithms are complex and resource-intensive, requiring multiple iterations of data engineering, visualization, training, testing, and validation, and must be repeated each time a new algorithm is needed.
Innovation Solution
A machine learning algorithm recommendation platform that uses historical data and multi-target classification and regression models to predict the most suitable machine learning algorithm and corresponding workspace configuration for a given task, reducing the need for repetitive evaluation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple iterations of data engineering, visualization, training, testing and validation are performed with different algorithms, then the selection of machine learning algorithm is improved, but the computational resources consumed increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical machine learning workspace metrics data in a database. This historical data is prepared and organized beforehand to enable efficient querying and analysis when making algorithm selection recommendations, avoiding the need to re-perform all data engineering and validation steps from scratch.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw historical data and the algorithm selection recommendation. This intermediary model processes the historical workspace metrics data and transforms it into actionable recommendations, reducing the computational burden of directly analyzing all historical data for each new algorithm selection.
2Measurement precision
If the selection process is repeated each time a new machine learning algorithm is needed, then the algorithm selection is optimized for the specific task, but the time required increases
Solution Approach 1:
The system performs preliminary action by pre-processing and storing historical machine learning workspace metrics data in a database. This historical data is prepared and organized beforehand to enable efficient querying and analysis when making algorithm selection recommendations, avoiding the need to re-perform all data engineering and validation steps from scratch.
Solution Approach 2:
The patent uses copying by creating a simplified representation of the complex algorithm selection process through a machine learning model trained on historical data. This model captures the essential patterns and relationships from previous algorithm selections, allowing it to quickly generate recommendations for new tasks without re-executing the full complex evaluation process.
3Use of energy by moving object
If historical machine learning workspace metrics data is used to predict algorithm and workspace configuration, then the computational resources required are reduced, but the complexity of the prediction system increases
Solution Approach 1:
The system segments the complex prediction task into separate components: data collection, data preprocessing, model training, and recommendation generation. Each component handles a specific aspect of the prediction process, making the overall system more manageable and easier to implement despite the increased functionality.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a mediator between the raw historical data and the algorithm selection recommendation. This intermediary model processes the historical workspace metrics data and transforms it into actionable recommendations, reducing the computational burden of directly analyzing all historical data for each new algorithm selection.
Data Source
AI summary
A method comprises receiving a request to predict at least one machine learning algorithm to perform one or more tasks and to predict a configuration of one or more workspaces in which the at least one machine learning algorithm is to be executed. Using the one or more machine learning models, the at least one machine learning algorithm and the configuration of the one or more workspaces are predicted in response to the request. The one or more workspaces are configured based, at least in part, on the predicted configuration.