Serverless ML refers to the deployment of machine learning models in a cloud environment without the need for managing server infrastructure. This approach allows data scientists and developers to focus on building and deploying models while the cloud provider handles the underlying resources. Serverless architectures, such as AWS Lambda and Google Cloud Functions, enable quick scaling and cost efficiency, as users only pay for the compute time they consume.

One of the key advantages of serverless ML is its ability to dynamically allocate resources based on demand. For instance, Amazon SageMaker Serverless Inference allows for the deployment of ML models for inference without pre-provisioning servers, making it suitable for applications with variable traffic patterns ². However, it’s important to note that serverless functions may have limitations regarding resource constraints, such as CPU and RAM, which can affect the performance of larger models ³.

Overall, serverless ML simplifies the deployment process and can significantly reduce costs, making it an attractive option for many organizations looking to implement machine learning solutions.