ML Model Specification Converter for IDE Integration
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Solution Overview
Problem
Existing software development environments require significant configuration and depart from object-oriented programming paradigms, creating a barrier for developers to integrate machine learning models, which are often represented as opaque resources rather than first-class objects.
Innovation Solution
Integrating machine learning models into interpreted software development environments by using a standard specification for description, creating derived data for indexing names and keywords, and employing mechanisms like specification converters and OOP code generators to provide features like syntax checking and auto-completion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine learning models are integrated into interpreted software development environments, then developers can leverage productivity features like syntax checking and auto-completion, but the integration requires significant configuration and departure from object-oriented programming paradigms
Solution Approach 1:
The patent introduces a model specification and converter as an intermediary layer between machine learning models and the interpreted software development environment. The converter transforms ML model specifications into a format compatible with the IDE's object-oriented paradigm, allowing developers to use productivity features without requiring the IDE itself to fundamentally change or require complex configuration.
Solution Approach 2:
The patent changes the representation parameters of machine learning models by defining a standard model specification that describes models in terms compatible with interpreted environments. This specification includes parameters like model inputs, outputs, and computation graphs in a format that can be indexed and manipulated as first-class objects rather than opaque resources.
2Productivity
If machine learning models are represented as opaque resources, then the software development environment maintains simplicity, but developers cannot leverage productivity features like syntax checking and error detection
Solution Approach 1:
The patent segments the machine learning model representation into distinct components: a model specification (describing inputs, outputs, and computation), derived data (for indexing names and keywords), and the actual model implementation. This segmentation allows the IDE to apply productivity features to the specification and derived data while keeping the underlying model implementation separate.
Solution Approach 2:
The patent performs preliminary actions by automatically generating derived data from the model specification before the developer needs to use the model. This derived data includes indexed names and keywords that enable syntax checking and auto-completion, so these productivity features are already prepared and available when the developer writes code.
Data Source
AI summary
The subject technology provides for parsing a line of code in a project of an integrated development environment (IDE). The subject technology executes indirectly, using the interpreter, the parsed line of code. The interpreter references a translated source code document generated by a source code translation component from a machine learning (ML) document written in a particular data format. The translated source code document includes code in a chosen programming language specific to the IDE, and the code of the translated source code document is executable by the interpreter. Further the subject technology provides, by the interpreter, an output of the executed parsed line of code.