Planning and Scheduling

Pipelines in AI infrastructure refer to automated sequences that connect various machine learning tasks, starting from data ingestion, preprocessing, model training, evaluation, and ending with deployment. They streamline the entire process, ensuring consistency, efficiency, and reproducibility in ML workflows. Pipelines enable seamless transition between stages, reduce manual intervention, and facilitate scalable deployment of AI models.