Data science environment architecture using SageMaker

Data scientists use data science environments to iterate different data science experiments with various datasets and algorithms. These environments require essential tools like Jupyter Notebook to author and execute code, data processing engines for handling large-scale data processing and feature engineering, and model training services for training models at scale. Additionally, an effective data science environment should include utilities for managing and tracking different experimentation runs, enabling researchers to organize and monitor their experiments effectively. To manage artifacts such as source code and Docker images, the data scientists also need a code repository and a Docker container repository.

The following diagram illustrates a basic data science environment architecture that uses Amazon SageMaker and other supporting services:

Figure 8.2: Data science environment architecture

SageMaker has...