Argo Workflows

Argo Workflows is an open-source, container-native workflow engine for orchestrating parallel jobs on Kubernetes, particularly suited for MLOps pipeline orchestration.

Use it when

• •Running ML workflows on Kubernetes clusters.
• •Orchestrating complex, multi-step ML pipelines.
• •Need parallel execution of ML training jobs.
• •Working with containerized ML workloads.
• •Implementing CI/CD for ML models in Kubernetes environments.
• •Managing compute-intensive ML jobs that require scaling.
• •Building cloud-agnostic ML pipelines.
• •Integrating with existing Kubernetes infrastructure.

Watch out

• ⚠Kubernetes expertise required: Steep learning curve for non-Kubernetes users.
• ⚠YAML complexity: Workflows defined in YAML can become complex.
• ⚠Local debugging challenges: Requires Kubernetes for local development.
• ⚠Limited ML-specific features: Lacks experiment tracking, hyperparameter optimization.
• ⚠Configuration overhead: Requires additional tools (MLflow, etc.) for complete MLOps.
• ⚠Resource management: DevOps teams concerned about compute/storage scaling demands.

Available in stages

Installation

Example stacks