Serverless ML
Serverless ML is a cloud computing approach that enables developers to build, deploy, and scale machine learning models without managing underlying infrastructure like servers or clusters. It leverages serverless platforms (e.g., AWS Lambda, Google Cloud Functions) and ML services (e.g., AWS SageMaker, Azure Machine Learning) to automate provisioning, scaling, and maintenance, allowing focus on model development and inference. This paradigm supports event-driven ML workflows, such as real-time predictions or batch processing triggered by data events.
Developers should use Serverless ML for cost-effective, scalable ML applications where infrastructure management is a bottleneck, such as in startups or projects with variable workloads. It's ideal for real-time inference APIs, automated data pipelines, or proof-of-concept models that require rapid deployment without operational overhead. This approach reduces time-to-market and operational costs by eliminating server provisioning and scaling tasks.