Template-Based Deployment of Data Science Environments
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Solution Overview
Problem
Existing technologies face challenges in efficiently deploying and managing data science environments for multiple data science applications, lacking the ability to quickly and easily configure and manage deployments, and often require multiple applications with different interfaces and functionalities.
Innovation Solution
A new technology facilitates the streamlined setup and deployment of data science environments using predefined, user-selectable templates and configuration data, allowing users to select and configure data science applications, allocate resources, and manage deployments through an environment deployment subsystem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional methods are used to deploy data science environments for multiple applications, then each application can be deployed with its own interface and functionality, but the time and effort required for configuring and deploying each environment is excessive and inefficient
Solution Approach 1:
The patent applies preliminary action by pre-configuring deployment templates with all necessary environment specifications, dependencies, and configuration parameters before deployment is needed. These templates store predefined settings for computing resources, software packages, and environment variables, allowing rapid instantiation of data science environments without manual configuration during deployment time.
Solution Approach 2:
The patent uses copying by creating reusable deployment templates that can be replicated across multiple applications. Instead of configuring each environment from scratch, the system copies proven template configurations and customizes them for specific applications, significantly reducing configuration time and ensuring consistency across environments.
2Adaptability or versatility
If multiple separate applications are used to manage different data science applications, then each application can have specialized functionality, but the complexity of managing multiple interfaces and configurations increases significantly
Solution Approach 1:
The patent applies universality by designing a single deployment subsystem that can handle multiple types of data science applications through a unified interface. The system uses parameterized templates that adapt to different application requirements (e.g., machine learning, data analysis, statistical modeling) while maintaining consistent deployment workflows, eliminating the need for multiple specialized tools.
Solution Approach 2:
The patent uses segmentation by dividing the deployment configuration into modular template components (computing resources, software dependencies, environment variables, access credentials). Each component can be independently configured and reused across different applications, making the system adaptable to various needs while keeping overall management simple through modular assembly.
3Adaptability or versatility
If manual configuration is used for each data science environment, then customization can be achieved, but the effort and resources required for setup and deployment become excessive
Solution Approach 1:
The patent applies preliminary action by pre-defining configuration parameters and environment specifications in templates before deployment. Common configurations for computing resources, software packages, and security settings are established in advance, allowing users to deploy customized environments by simply selecting and modifying template parameters rather than configuring everything manually.
Solution Approach 2:
The patent uses an intermediary approach by introducing deployment templates as intermediate artifacts between application requirements and actual environment provisioning. The templates act as mediators that translate high-level application needs into detailed configuration specifications, reducing the effort required for manual customization while ensuring proper environment setup.
Data Source
AI summary
An example client device is configured to (i) display an interface for deploying a new data science environment at a computing platform, (ii) receive, via the interface, a user selection of (a) a given data science application from a list of data science applications that is presented by the interface and (b) one or more deployment configuration parameters from a set of deployment configuration parameters that is presented by the interface, (iii) transmit, to the computing platform, a first network-based communication comprising an indication of the user selection of (a) the given data science application and (b) the one or more deployment configuration parameters, and (iv) receive, from the computing platform, a second network-based communication comprising an indication that the new data science environment has been deployed based on the user selection of the (a) the given data science application and (b) the one or more configuration parameters.