Starting with Amazon SageMaker does not require deep infrastructure knowledge, but having a clear understanding of the setup steps can greatly improve your experience. The setup process begins in the AWS Management Console, where users can access SageMaker and choose among multiple tools, such as Studio, Studio Classic, or Jupyter notebooks. These environments provide users with everything needed to begin building machine learning models, including compute resources and preconfigured libraries.
Before launching any notebook environment, users must define roles and permissions using AWS Identity and Access Management (IAM). These permissions allow SageMaker to access necessary data from Amazon S3, communicate with training jobs, and deploy models to endpoints. Users can choose among predefined roles or create custom roles depending on the security requirements. After that, they can configure networking settings to ensure access is restricted to specific VPCs if necessary.
Costs can vary depending on the resources selected during setup. Users can select instance types that best match their workload size—starting from smaller CPUs for development to high-powered GPUs for training. AWS provides billing dashboards to help monitor usage, but cost forecasting and right-sizing can be difficult without proper experience. It’s also important to shut down unused instances to avoid unexpected charges.
That’s why many companies rely on Skyloop Cloud. As an AWS Advanced Tier Services Partner, we help organizations across Dubai, Istanbul, and London simplify their AI infrastructure. We guide clients in selecting the right compute instances, managing IAM roles, and designing cost-efficient architectures. Our team works closely with both startups and large enterprises, ensuring SageMaker is configured correctly from the beginning to support scalability and security requirements without overspending.
Setting up SageMaker involves several decisions that can influence performance and cost. With proper guidance, businesses can establish a solid ML environment that is secure, scalable, and financially efficient. In the next part of our series, we will explore how SageMaker supports no-code and low-code tools for faster development and experimentation.
