✅ SageMaker: SageMaker is called an Infrastructure Layer because it provides raw computing resources, model training capabilities, and deep control over ML workloads, requiring technical expertise to manage. 1️⃣ Direct Control Over Compute & Models SageMaker provides full control over infrastructure , allowing data scientists and ML engineers to train, fine-tune, and deploy models using dedicated compute resources (e.g., GPU instances). Users choose instance types, frameworks (TensorFlow, PyTorch, MXNet), and manually configure infrastructure settings. 2️⃣ Custom Model Training & Deployment Users can bring their own models or fine-tune pre-trained models with custom datasets. SageMaker provides end-to-end model lifecycle management , from data processing to monitoring deployed models. 3️⃣ Requires ML Expertise & Engineering Effort SageMaker is designed for data scientists, ML engineers, and developers w...