Training and deploying a TensorFlow and Keras model with the SageMaker Python SDK

Performing the training and deployment of a custom TensorFlow and Keras model with SageMaker is fairly straightforward. Step 1 involves creating the entrypoint script where our custom neural network and training logic are defined and coded. Step 2 involves using this script as an argument to the TensorFlow estimator from the SageMaker Python SDK to proceed with the training and deployment steps.

In this recipe, we will focus on step 2 and proceed with the training and deployment of our custom TensorFlow and Keras neural network model in SageMaker. If you are looking for step 1, feel free to check the previous recipe, Preparing the entrypoint TensorFlow and Keras training script.

Getting ready

This recipe continues from Preparing the entrypoint TensorFlow and Keras training script.

How to do it

The instructions in this recipe focus on using the custom entrypoint training script from the...