Nilesh Barla's article examines three prominent ML task orchestration tools: Argo, Airflow, and Prefect, each of which aids in organizing and executing various phases of AI systems like data collection, modeling, and deployment. Argo, a Kubernetes-native tool, excels in parallel processing and scalability but requires familiarity with YAML for configuration. Airflow, known for its rich scheduling and Python integration, supports static workflows and diverse third-party tools. Prefect, notable for its flexibility and dynamic workflows, also supports Python and third-party integrations, offering enhanced UI and security features. The article compares these tools based on core concepts, features, scalability, and flexibility, providing guidance on selecting the right tool based on project requirements and existing infrastructure.