Definition
Orchestration of ML pipelines refers to the automated management and coordination of complex machine learning workflows, ensuring that each step from data ingestion to model deployment runs smoothly and efficiently. It involves defining, scheduling, monitoring, and managing dependencies between various tasks like data preprocessing, model training, evaluation, and deployment. This automation is crucial for enabling reproducible research, faster iteration cycles, and reliable production deployment of machine learning models. For instance, an orchestration tool might automatically trigger a model retraining process when new data becomes available or when model performance degrades below a certain threshold. Unlike simple scripting, orchestration provides robust error handling, logging, and scalability for the entire ML lifecycle. This concept is frequently covered in advanced cloud certification exams like AWS Certified Solutions Architect Professional.