Training a model using SageMaker
As mentioned in the Utilizing SageMaker for ETL section of Chapter 5, Data Preparation in the Cloud, the motivation of SageMaker is to help engineers and researchers focus on developing high-quality DL pipelines without worrying about infrastructure management. SageMaker manages data storage and computational resources for you, allowing you to utilize a distributed system for model training with minimal effort. In addition, SageMaker supports streaming data to your models for inferencing, hyperparameter tuning, and tracking experiments and artifacts.
SageMaker Studio is the place where you define the logic for your model. The SageMaker Studio notebooks allow you to quickly explore the available data and set up model training logic. When model training takes too long, scaling up to use multiple computational resources and finding the best set of hyperparameters can be efficiently achieved by making a few modifications to the infrastructure’...
