src/data/roadmaps/mlops/content/orchestration@fes7M--Y8i08_zeP98tVV.md
ML orchestration refers to the process of managing and coordinating the various tasks and workflows involved in the machine learning lifecycle, from data preparation and model training to deployment and monitoring. It involves integrating multiple tools and platforms to streamline operations, automate repetitive tasks, and ensure seamless collaboration among data scientists, engineers, and operations teams. By using orchestration frameworks, organizations can enhance reproducibility, scalability, and efficiency, enabling them to manage complex machine learning pipelines and improve the overall quality of models in production. This ensures that models are consistently updated and maintained, facilitating rapid iteration and adaptation to changing data and business needs.
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