Cluster Engine
Orchestration
Orchestration for MLOps refers to the automated coordination, scheduling, and management of various interconnected processes and workflows in machine learning operations, ensuring seamless integration across the ML lifecycle from deployment through monitoring.
Key Components
- Workflow Automation – Automates repetitive tasks like data preprocessing, model training, evaluation, and deployment.
- Pipeline Management – Executes end-to-end ML workflows in correct sequence.
- Resource Allocation – Assigns CPUs, GPUs, or TPUs efficiently to various tasks.
- Version Control – Tracks data, models, and code versions.
- Monitoring and Logging – Observes model performance and system health.
- Error Handling and Retry Mechanisms – Detects failures and automatically retries.
Common Tools
- Kubernetes
- Apache Airflow
- Kubeflow
- MLflow
- Prefect
- Dagster
FAQ
It's the automated coordination and scheduling of all the moving parts in the ML lifecycle—data prep, training, evaluation, deployment, and monitoring—so they run reliably, in the right order, and at scale.