Creating notebooks in Amazon SageMaker
If you are working with ML, then you need to perform actions such as storing data, processing data, preparing data for model training, model training, and deploying the model for inference. They are complex, and each of these stages requires a machine to perform the task. With Amazon SageMaker, life becomes much easier when carrying out these tasks.
What is Amazon SageMaker?
SageMaker provides training instances to train a model using the data and provides endpoint instances to infer by using the model. It also provides notebook instances running on the Jupyter Notebook to clean and understand the data. If you are happy with your cleaning process, then you should store the cleaned data in S3 as part of the staging for training. You can launch training instances to consume this training data and produce an ML model. The ML model can be stored in S3, and endpoint instances can consume the model to produce results for end users.
If you draw...
