Deployment

Deployment is the process of making your predictors available to clients. This section provides an overview of the deployment process and the tools you can use to deploy your predictors.

Why do you need deployment?

Deployment is an essential step in the machine learning lifecycle. It allows you to make your predictors available to clients so they can use them to make predictions. The deployment process involves packaging your predictor, uploading it to the runtime, and configuring it to make predictions in real-time.

What is deployment?

Deployment is the process of making your predictors available to clients. It involves packaging your predictor, uploading it to the runtime, and configuring it to make predictions in real-time. The deployment process is essential for making your predictors accessible to clients so they can use them to make predictions.

How does deployment work?

The deployment process involves several steps:

• Configuration: Configure your predictor to work with the runtime.
• Packaging: Package your predictor and any required dependencies.
• Push: Push your predictor configuration to the runtime.
• Start: Start your predictor to make it available for predictions.