CHATBOT FOR DEFINING A MACHINE LEARNING (ML) SOLUTION

Smart overview of the Invention

The disclosure outlines a system for an intelligent assistant, such as a chatbot, designed to help users create machine learning systems. This system is particularly beneficial for users lacking expertise in software development or machine learning. Users can interact with the chatbot through various interfaces, including aural, textual, or graphical. The chatbot translates natural language inputs into structured machine learning solutions, enabling users to develop, train, refine, and compile machine learning models as executable code without needing to be data scientists.

Background

Machine learning applications span various fields, but creating these applications typically demands significant programming knowledge. Existing tools often require data scientists to manually reconcile data schemas or rely on extensive programming to build custom models. Additionally, current systems may focus primarily on model performance without considering adaptability to changing data metrics. This invention addresses these challenges by providing a more intuitive interface and automated processes to streamline the development of machine learning applications.

Features

The proposed platform generates a library of components for creating machine learning models and applications. It allows users to develop applications without needing in-depth knowledge of network infrastructure or coding. The platform can analyze data and user-defined prediction goals to select suitable library components and APIs. It also enables monitoring and feedback mechanisms for model adjustments, allowing models to be trained, tested, and compiled for export as standalone executable code.

Chatbot Functionality

A key component is the chatbot, which provides an intuitive interface for developing machine learning applications. It translates natural language into structured machine learning solutions and assists users in data location, solution selection, and environment recommendations. The system also includes a discovery feature that maps client data schemas to machine learning model classifications, ensuring solutions are client-agnostic and adaptable to various use cases.

Advanced Techniques

The system leverages existing data ontologies for high-precision service searches and pipeline composition with minimal human intervention. It can generate new ontologies for datasets lacking them, enhancing precision in service selection. An adaptive pipelining service can incorporate new models and parameters, testing them offline against ground truth data. Successful models and parameters are auto-promoted to production, ensuring ongoing optimization and adaptation of machine learning applications.