Configurable Data Science Model Templates for Faster Deployment
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Solution Overview
Problem
The process of creating and deploying data science models is time-consuming, complex, and labor-intensive, requiring collaboration among multiple professionals and lacking efficient deployment and management capabilities in existing software applications.
Innovation Solution
A computing platform facilitates the creation and deployment of data science models using predefined, user-selectable templates and configuration parameters, allowing users to define input datasets, output locations, and computing resources, with the ability to track and manage deployed models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data science models are created and deployed using traditional manual processes, then model accuracy and customization can be achieved, but the process becomes time-consuming, complex, and labor-intensive
Solution Approach 1:
The patent applies preliminary action by pre-configuring deployment templates with model code, data sources, and execution parameters before actual deployment. Users select from pre-defined templates that already contain validated configurations, eliminating the need to manually set up each deployment from scratch. This resolves the contradiction by maintaining customization capability through template selection while dramatically reducing deployment time and complexity.
Solution Approach 2:
The patent uses copying by creating reusable deployment templates that can be instantiated multiple times with different parameters. Once a template is configured with specific model code and data sources, it can be copied and deployed repeatedly without reconfiguring the same settings. This resolves the contradiction by maintaining model accuracy through template reuse while reducing labor-intensive manual configuration for each deployment.
2Reliability
If multiple professionals collaborate on model creation manually, then comprehensive model quality can be achieved, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The system performs preliminary validation and configuration checks within deployment templates before deployment occurs. Model code, data sources, and parameters are pre-validated to ensure quality requirements are met, eliminating the need for extensive manual review cycles. This maintains model reliability while significantly reducing the time lost to collaborative review processes.
Solution Approach 2:
The deployment system performs self-validation of model configurations against predefined quality criteria. The automated system checks model code, validates data source connectivity, and verifies parameter consistency without requiring manual intervention from multiple professionals. This maintains model quality through automated validation while eliminating time-consuming human review cycles.
3Ease of operation
If existing software applications are used for model deployment, then basic functionality is available, but efficient deployment and management capabilities are lacking
Solution Approach 1:
The deployment template system provides multi-functionality by consolidating model code management, data source configuration, parameter setting, and deployment execution into a single unified interface. Users can perform multiple deployment tasks through one system rather than switching between multiple tools, improving both ease of operation and deployment efficiency simultaneously.
Data Source
AI summary
An example computing platform is configured to (a) cause a client device associated with a user to display an interface for deploying a new data science model, where the interface presents the user with a list of deployment templates, and where each of the deployment templates includes data specifying (i) a respective executable model package and (ii) a respective set of execution instructions for the respective executable model package, (b) receive, from the client device, data indicating (i) a user selection of a given deployment template for use in deploying the new data science model and (ii) a given set of configuration parameters for use in deploying the new data science model, and (c) use the given executable model package, the given set of execution instructions, and the given set of configuration parameters to deploy the new data science model.