I want to implement a machine learning model that integrates seamlessly with an application (e.g., a custom app or productivity software). The goal is for the model to:
- Utilize the application’s user data.
- Answer user queries based on the specific context of the application’s data, similar to how tools like GitHub Copilot integrate with development environments or Microsoft Copilot integrates with Microsoft Office applications.
I am looking for guidance on:
- Model Selection: How to choose the right machine learning or large language model (LLM) for this purpose.
- Integration: Setting up the integration between the model and the application.
- Model Training: How to train the model so that it can effectively utilize user-specific data and answer queries based on that data. Which techniques are best suited for this purpose, and how can I tailor the training process to the application’s context?
- Data Preprocessing: Best practices for preparing and managing user data securely.
- Hosting and Deployment: Strategies for hosting and deploying the model to ensure scalability and optimal performance.
Any examples, frameworks, or references to similar implementations would be greatly appreciated.