Introduction
Argo Workflows is a powerful, open-source container-native workflow engine for Kubernetes that enables teams to define, schedule, and manage multi-step workflows as code. Argo Workflows is designed to run DAGs (Directed Acyclic Graphs) of steps, where each step runs in its own container. It offers robust features like parallel execution, retries, caching, and artifact management—making it ideal for ML training pipelines, feature engineering, prompt evaluations, model validation, and batch inference.
Key Benefits of Using Argo Workflows includes:
Kubernetes-Native Execution: Each step in a workflow runs as a Kubernetes pod, fully leveraging the Cake platform’s container infrastructure, autoscaling, and scheduling policies.
Declarative YAML Configuration: Workflows are defined via version-controlled YAML specs, enabling reproducibility, auditability, and collaboration across teams.
DAG and Step-Level Control: Supports linear, branching, and conditional workflows, with fine-grained control over dependencies, parameters, and environment variables.
Artifact Passing and Volume Sharing: Easily passes files, model checkpoints, or metrics between workflow steps using object stores or shared volumes.
Observability and UI: Offers a built-in UI and CLI for tracking workflow executions, debugging failures, and visualizing execution graphs in real time.
Use Cases
Argo Workflows is leveraged across several mission-critical systems, including:
Model training and evaluation: Automating multi-step pipelines for fine-tuning LLMs, running evaluation metrics, and benchmarking across versions.
Data preprocessing and feature generation: Running large-scale Spark or Python-based transformations in a reproducible, containerized environment.
RAG and agent evaluation: Defining repeatable pipelines for prompt tuning, LLM behavior testing, and multi-agent system evaluation.
Batch jobs and scheduled experiments: Handling long-running inference, retraining, or metrics logging workflows on a regular cadence or via event triggers.
Argo Workflows integrates tightly with other components in Cake’s ML platform stack, such as Kubeflow Pipelines, MLflow, TensorBoard, LangGraph, and Argo CD for GitOps-based deployment. Argo Workflows enables reliable, modular, and scalable orchestration of complex ML and data workflows—empowering teams to iterate faster, scale effortlessly, and maintain full visibility into every step of the pipeline lifecycle.