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 leverages cloud computing to dynamically allocate resources based on demand, enabling efficient scaling and reducing operational overhead. With serverless ML, models can be executed as functions in response to events, making it ideal for applications with variable workloads. This approach not only simplifies the deployment process but also enhances the accessibility of ML solutions, allowing businesses to focus on model development and optimization rather than infrastructure management.