Integrated ML Model Creation in IDE
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Solution Overview
Problem
Existing machine learning technologies require separate tools and programming languages for model creation, making it difficult for developers to integrate and test machine learning models within the same integrated development environment as application development, leading to inefficiencies in training and seamless integration.
Innovation Solution
A user-friendly interface for creating machine learning models within an integrated development environment, allowing developers to select templates for image, text, sound, and tabular data analysis, enabling drag-and-drop data input, and providing a graphical workflow for training and testing models, with options for live data capture and seamless integration into applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate machine learning tools and programming languages are used for model creation, then machine learning models can be developed with specialized functionality, but integration and testing within the application development environment becomes difficult and inefficient
Solution Approach 1:
The patent combines machine learning model creation capabilities directly into the application development environment by integrating a machine learning template selector, data intake interface, and model training functionality within the same development platform. This allows developers to create, train, and integrate machine learning models without switching between separate tools, thereby improving both integration reliability and ease of operation.
Solution Approach 2:
The integrated development environment is enhanced to perform multiple functions: traditional application development, machine learning model creation, data intake and preprocessing, model training, and model testing all within a single platform. This multi-functional approach eliminates the need for separate specialized tools while maintaining the capabilities needed for reliable model development and integration.
2Adaptability or versatility
If separate tools are used for machine learning model development, then specialized machine learning functionality can be achieved, but the development process becomes more complex and time-consuming
Solution Approach 1:
The patent merges machine learning development tools with the application development environment, consolidating multiple separate tools into a single integrated platform. This reduces the overall complexity of the development process while preserving specialized machine learning functionality through dedicated templates and interfaces within the unified environment.
Solution Approach 2:
The integrated development environment provides universal capabilities that cover both traditional application development and machine learning model creation. By incorporating data intake, template selection, model training, and testing functionalities within the same platform, the system maintains adaptability for various machine learning tasks while reducing the complexity associated with managing multiple separate tools.
3Reliability
If separate machine learning tools are used, then specialized model training capabilities can be provided, but seamless integration into applications becomes difficult
Solution Approach 1:
The patent merges the model training and integration processes by allowing developers to create and train machine learning models directly within the application development environment. The trained models are automatically integrated into the application code, eliminating the time-consuming process of exporting models from separate tools and manually integrating them, thereby improving integration reliability and reducing time loss.
Solution Approach 2:
The system performs preliminary actions by providing pre-configured machine learning templates for different data types (images, text, sound, tabular data) and automatically setting up the necessary integration structures. This preliminary preparation reduces the time required for integration and testing, as the framework is already in place when the model is trained and ready for deployment.
Data Source
AI summary
Embodiments of the present disclosure present devices, methods, and computer readable medium for techniques for creating machine learning models. Application developers can select a machine learning template from a plurality of templates appropriate for the type of data used in their application. Templates can include multiple templates for classification of images, text, sound, motion, and tabular data. A graphical user interface allows for intuitive selection of training data, validation data, and integration of the trained model into the application. The techniques further display a numerical score for both the training accuracy and validation accuracy using the test data. The application provides a live mode that allows for execution of the machine learning model on a mobile device to allow for testing the model from data from one or more of the sensors (i.e., camera or microphone) on the mobile device.