Serverless Machine Learning (ML) is an innovative approach that allows developers to deploy and manage ML models without the need for traditional server infrastructure. This paradigm shifts the focus from server management to model development and deployment, enabling automatic scaling based on demand. With serverless ML, resources are allocated dynamically, which helps optimize costs and performance. Major cloud providers, such as Amazon with its SageMaker Serverless Inference, have begun integrating serverless computing into the ML lifecycle, making it easier to serve models for inference. This approach is particularly beneficial for applications with variable workloads, enhancing efficiency and reducing operational overhead.