ML Orchestration
ML Orchestration is the process of coordinating and automating the different tasks, tools, and systems involved in developing, deploying, and managing machine learning workflows.
What is ML Orchestration?
ML orchestration connects and manages steps such as data preparation, model training, evaluation, deployment, and monitoring. Orchestration tools can schedule workflows, manage dependencies between tasks, allocate resources, and ensure that each stage runs in the correct order.
Why is ML Orchestration Important?
ML orchestration helps teams automate complex machine learning workflows and make them more consistent and reproducible. It reduces manual work, improves resource management, and makes it easier to scale and maintain machine learning pipelines across development and production environments.
Common use cases
ML orchestration is commonly used for automated training pipelines, data processing, model evaluation, deployment workflows, retraining, monitoring, and managing production machine learning pipelines.