Custom Model Deployment

Custom model deployment is a crucial step in the process of integrating machine learning models into your product. It involves the implementation and deployment of a tailor-made model that has been specifically trained to address the unique requirements of your application. By deploying a custom model, you can leverage the power of machine learning to enhance your product's functionality, improve accuracy, and provide personalized experiences to your users.

With the Katonic Custom deployment feature, you can deploy any type of model from a simple to a complex one.

To deploy a custom model, you must prepare your code in the template accepted by the platform. You need a launch.py file, requirements.txt file, schema.py file, and files required for your model to run smoothly. A detailed description of the required file formats is described in each section.

Once you are ready with these files, head to the Deploy section of the platform, no need to break your head setting up the infrastructure, just configure from the deployment panel and hit the deploy button to make your model accessible to the outside world as a scalable and reliable tool with a capability of handling the expected user load.

After the deployment environment is ready, you can proceed with integrating the custom model into your product in the form of an API that enables seamless communication between your product and the deployed model. The APIs provide a user-friendly interface for your product to send input data to the model and receive predictions or insights in return.

To ensure the stability and performance of your deployed model, it is essential to thoroughly test it in different scenarios and against various use cases. This testing phase helps identify and address any issues or limitations that may arise during real-world usage. Regular monitoring and maintenance are also crucial to keep the deployed model up-to-date, address performance bottlenecks, and incorporate improvements or updates as needed.

To get yourself comfortable with deploying different types of models, head to the respective section under the documentation.

Refer to the Katonic deployment documentation to get familiar with deploying various types of models