Summary

In this chapter, we trained and deployed ML models using the SageMaker Python SDK. We started by using the MNIST dataset (training dataset) and SageMaker’s built-in Image Classification Algorithm to train an image classifier model. After that, we took a closer look at the resources used during the training step by using the Debugger Insights Dashboard available in SageMaker Studio. Finally, we performed a second training experiment that made use of several features and options available in SageMaker, such as managed spot training, checkpointing, and incremental training.

In the next chapter, we will dive deeper into the different deployment options and strategies when performing model deployments using SageMaker. We will be deploying a pre-trained model into a variety of inference endpoint types, including the real-time, serverless, and asynchronous inference endpoints.