Amazon SageMaker offers a flexible set of environments for different stages of the Machine Learning lifecycle. Whether users prefer an interactive graphical interface or command-line control, SageMaker provides a suitable workspace. These environments are essential for organizing experiments, managing resources, and collaborating with teammates effectively.
At the center of the SageMaker experience is SageMaker Studio. It is an integrated development environment (IDE) designed for machine learning workflows. Studio provides tools for preparing data, building and training models, and deploying them—all from a single interface. Studio Classic, the earlier version, still supports many of the same features but lacks the enhanced user experience of Studio. For those who prefer coding in notebooks, SageMaker offers JupyterLab and Notebook Instances. JupyterLab is the more modern and customizable option, while Notebook Instances are managed environments with built-in compute resources.
Each of these environments serves different users. Data scientists may opt for JupyterLab’s flexibility, while analysts might find SageMaker Studio’s visual tools more intuitive. Studio supports collaboration through shared spaces, enabling teams to work on the same project with centralized access and control. All environments integrate with other AWS services, such as Amazon S3 for data storage and AWS Identity and Access Management (IAM) for secure access.
This is where Skyloop Cloud provides critical support. As an AWS Advanced Tier Services Partner, we help clients across EMEA—via our offices in Dubai, Istanbul, and London—choose the right development environments. We guide startups through Studio setup and configuration, and assist enterprises in migrating from Notebook Instances to shared Studio Spaces. Our experience ensures your teams adopt tools that align with their technical maturity, compliance requirements, and collaboration needs. We also help monitor resource usage to keep SageMaker costs predictable.
SageMaker’s diverse environments support a wide range of users and workflows. They offer the foundation for a streamlined and productive machine learning pipeline. In the next article, we’ll look at how to deploy trained models for real-world use—safely, efficiently, and at scale.
