NLP Preprocessing DSL for Cross-Environment Deployment
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Solution Overview
Problem
The process of translating NLP models from offline to online computing environments is time-consuming and prone to errors due to differences in programming languages, libraries, and software modules, requiring manual code translation and validation.
Innovation Solution
A framework utilizing a domain-specific language (DSL) to describe preprocessing pipelines for NLP models, enabling seamless deployment across environments by generating configuration data that defines preprocessing modules and their sequences, allowing validation without code translation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual code translation is performed to deploy NLP models from offline to online environments, then the model can be deployed in production, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent introduces an intermediary translation layer that automatically converts code between offline and online environments. This intermediary system handles the code translation process, eliminating manual intervention and significantly reducing deployment time while maintaining accuracy. The translation layer acts as a mediator that understands both environments' requirements and automatically adapts the code.
Solution Approach 2:
The system performs preliminary actions by pre-configuring translation rules and environment mappings before deployment. By preparing the translation framework in advance and establishing the relationship between offline and online environments beforehand, the actual deployment process becomes much faster and requires minimal manual intervention.
2Productivity
If manual code translation is performed to deploy NLP models from offline to online environments, then the model can be deployed in production, but errors increase during the process
Solution Approach 1:
The automatic translation intermediary ensures reliability by systematically applying predefined translation rules and validation checks. This eliminates human errors associated with manual code translation, as the intermediary systematically handles variable mappings, function translations, and environment-specific adaptations with consistent accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms that validate translated code against the target online environment requirements. By checking the translated code for correctness and compatibility, and providing feedback for corrections, the system ensures high translation accuracy and reduces deployment errors.
3Adaptability or versatility
If different programming languages and libraries are used in offline and online environments, then data scientists have flexibility in model building, but production engineers face complexity in code translation
Solution Approach 1:
The translation system is designed with universality to handle multiple programming languages and libraries. It maintains a comprehensive mapping database that supports various offline languages (Python, R, MATLAB) and translates them to the online environment's required language. This universal approach allows data scientists to use their preferred tools while the system automatically manages the translation complexity.
Solution Approach 2:
The intermediary translation layer abstracts the complexity of multi-language support by providing a unified translation interface. Instead of requiring production engineers to manually handle language-specific translation rules, the intermediary automatically manages the complexity of converting between different programming paradigms, libraries, and syntax requirements.
4Ease of manufacture
If code translation is performed manually for each NLP model deployment, then customization is possible, but the process becomes time-intensive
Solution Approach 1:
The system implements self-service by enabling automatic self-translation of code during the deployment process. The translation intermediary autonomously handles the conversion of model code from offline to online environments without requiring manual intervention from production engineers. This self-service capability maintains customization while dramatically reducing deployment time.
Solution Approach 2:
The system performs preliminary setup by pre-configuring translation rules, environment mappings, and validation protocols before actual model deployment. This preliminary preparation enables rapid automated translation during deployment, eliminating the need for time-intensive manual code translation while preserving customization options.
Data Source
AI summary
A system performs operations that include receiving, via first computing environment, a request to process text data using a first natural language processing (NLP) model. The operations further include accessing configuration data associated with the NLP model, where the configuration data generated using a domain specific language that supports a plurality of preprocessing modules in a plurality of programming languages. The operations also include selecting, based on the configuration data, one or more preprocessing modules of the plurality of preprocessing modules, generating, based on the configuration data, a preprocessing pipeline using the one or more preprocessing modules, and generating preprocessed text data by inputting the text data into the preprocessing pipeline. The preprocessed text data is provided to the first NLP model.