AI pipelines

AI pipelines automate the full machine learning lifecycle: transforming raw data, training models (e.g., deep neural networks), and deploying them for continuous, scaled production inference.

An AI pipeline is the structured MLOps workflow that orchestrates the end-to-end development of an AI system. It systematically connects key stages: data ingestion, preprocessing (e.g., feature engineering), model training, evaluation, and deployment (e.g., via a REST API endpoint). This automation is critical for enterprise-grade AI, ensuring reproducibility and scalability across environments. For example, a pipeline can process petabytes of sensor data, train a new predictive maintenance model in 48 hours, and automatically push the updated model to 5,000 edge devices, significantly reducing downtime and manual oversight.

https://www.supermicro.com/en/solutions/artificial-intelligence/what-is-an-ai-pipeline