Data Research Analytics Engine for Automated Model Deployment
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Solution Overview
Problem
Conventional tools lack a unified platform for end-to-end modeling of financial applications, requiring inefficient manual rewriting of code and lacking data management and pipeline organization, especially under regulatory constraints.
Innovation Solution
A platform, language, and cloud agnostic research, analytics, and modeling module that automates development, testing, and productionizing pipelines, providing intuitive analytics and modeling capabilities with simplified deployment workflows, while maintaining control guardrails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional tools are used for data research and analytics, then manual code rewriting is required, but this process is inefficient and time-consuming
Solution Approach 1:
The system enables self-service analytics and modeling capabilities where users can independently develop, test, and deploy their own code without requiring manual rewriting by technology teams. The platform provides self-contained environments with pre-configured data access, analytics functions, and model deployment capabilities that users can leverage directly.
Solution Approach 2:
The platform acts as an intermediary layer between users and the production environment, providing a sandbox environment where code can be developed and tested before automated deployment. This intermediary platform handles the translation and validation of user code, eliminating the need for manual rewriting while maintaining control and governance.
2Reliability
If manual code rewriting is performed for productionizing, then control and governance can be maintained, but the process becomes complex and resource-intensive
Solution Approach 1:
The system performs preliminary actions by pre-configuring production environments, data connections, and model deployment frameworks before users write their code. Templates andboilerplate structures are prepared in advance, allowing users to focus on business logic while the platform handles infrastructure complexity and governance requirements.
Solution Approach 2:
The platform changes parameters by automatically adjusting technical configuration parameters based on user intent and governance requirements. Instead of users manually managing complex deployment parameters, the system automatically configures environment variables, data connection strings, model serving parameters, and other technical settings while maintaining control policies.
3Extent of automation
If a unified platform is implemented for end-to-end modeling, then automation and efficiency improve, but integration complexity and infrastructure requirements increase
Solution Approach 1:
The system merges multiple separate functions (data access, analytics, model training, testing, and deployment) into a single unified platform. By combining these previously separate tools and processes into one integrated environment, the system achieves end-to-end automation while the underlying complexity is abstracted away through a unified interface and consistent data model.
Solution Approach 2:
The platform provides universal capabilities that serve multiple functions through a single system. It can handle various types of data sources, support multiple analytics and modeling approaches, and accommodate different deployment scenarios all through the same interface and infrastructure, reducing overall system complexity despite the breadth of functionality.
4Ease of operation
If sandbox environments are provided for users, then independent development is enabled, but data management and pipeline organization become challenging
Solution Approach 1:
The system adds another dimension to sandbox environments by implementing hierarchical data management where user sandboxes operate at one level while centralized data catalogs and pipeline orchestration operate at another level. This multi-dimensional approach allows users to work independently in their sandboxes while the platform automatically manages data provenance, pipeline dependencies, and cross-user data sharing through a centralized coordination layer.
Data Source
AI summary
Various methods and processes, apparatuses or systems, and media for automating development, testing, and productionizing a pipeline for users are disclosed. A processor receives a request from a user to access an application, the request including user's credentials data; grants access to the application based on verifying the user's credentials data with prestored credentials data received by calling an authentication server; identifies the user's role within a computing environment; automatically presents a template that corresponds to the user's role allowing the user to write code to source data either by bringing the user's own data into the computing environment or by connecting to data that resides in a database; automatically integrates the written code with a continuous integration continuous delivery pipeline for production of a model; and deploys the model after training and testing the model while managing and maintaining all necessary guardrails from a control standpoint within the computing environment.