QML based RUL Forecast Using IOT Sensors

Before deploying the model, train it with your data using the algorithm training process. You're billed for software and SageMaker infrastructure costs only during training. Duration depends on the algorithm, instance type, and training data size. When training completes, the model artifacts save to your Amazon S3 bucket. These artifacts load into the model when you deploy for real-time inference or batch processing. For more information, see Use an Algorithm to Run a Training Job  .

Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .

Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .