- Develop Your RUL Model in MATLAB: Utilize MATLAB's extensive data processing and machine learning toolboxes to develop and validate your RUL prediction model.
- Convert Your Model for Deployment: Use MATLAB Compiler or MATLAB Compiler SDK to convert your model into a standalone application or a web app. This process involves creating a deployable archive of your MATLAB code.
- Design the User Interface (UI): For a standalone application, you can design a UI using MATLAB App Designer, which allows for drag-and-drop assembly of interfaces. For a web app, you might need additional web development skills or tools.
- Deploy the Application: Standalone applications can be distributed directly to end-users to run on their machines. Web apps can be hosted on MATLAB Web App Server or integrated into existing web infrastructure.
- Ensure Access to MATLAB Runtime: End-users do not need a MATLAB license to use the deployed app, but they will need the MATLAB Runtime, which is a free application available from MathWorks.
- For developing machine learning models, refer to the MATLAB documentation on Machine Learning: https://www.mathworks.com/help/stats/machine-learning-in-matlab.html.
- To learn about converting MATLAB code for deployment, see MATLAB Compiler: https://www.mathworks.com/help/compiler/index.html and MATLAB Compiler SDK: https://www.mathworks.com/help/compiler_sdk/index.html.
- For designing UIs, MATLAB App Designer is documented at: https://www.mathworks.com/help/matlab/app-designer.html.
- Information on MATLAB Runtime is available at: https://www.mathworks.com/products/compiler/matlab-runtime.html.