Serverless machine learning (ML) is an emerging paradigm that enhances operational capabilities by utilizing loosely coupled serverless technologies. This approach facilitates essential workflows like feature engineering, model training, and batch inference, which together yield outputs such as machine learning models and prediction logs. In a serverless environment, each stage of the ML pipeline operates as an independent process, streamlining data processing from raw input to final predictions while ensuring effective monitoring and observability of model performance. Overall, serverless ML empowers real-time model access and efficient AI deployment through coordinated workflows.
Serverless ML leverages decoupled technology for operational efficiency, allowing seamless coordination of workflows that generate models and enable AI-driven applications.
Machine learning pipelines consist of a series of operations that process data, transitioning from raw input to feature engineering, model training, and prediction services.
In serverless ML, each pipeline phase operates independently, creating stand-alone processes that utilize shared resources for enhanced monitoring and performance tracking.
The serverless architecture supports real-time model access, paving the way for efficient prediction services while maintaining observability through robust logging mechanisms.
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