Tasks are like templates in Argo which are used to define a specific function that needs to be executed. ![]() In Prefect, flow objects can be created using Python which provides flexibility and robustness to define complex pipelines. Prefect uses DAGs that are defined as flow object which uses Python. It leverages two concepts Flows and Tasks. Here is a table inspired by Ian McGraw’s article, which provides an overview of what these tools offer for orchestration and how they differ from each other in these aspects. In this article, we will explore three tools – Argo, Airflow, and Prefect, that incorporate these two properties and various others as well. But this is not the case every time, some of the tools are strictly contained within their derived environments, which does not bode well for users trying to integrate any third-party applications. If an orchestration tool can orchestrate various tasks from different tools, then it can be considered a good tool. ![]() This allows ML practitioners to incorporate various other tools that can be used to monitor, deploy, analyze and preprocess, test, infer, et cetera. DAG also enables tasks to be sequentially sound or arranged for proper execution and timely results.Īnother important property that these tools have is adaptability to agile environments. DAG enables tasks in a pipeline to be distributed parallelly to various other modules for processing, this offers efficiency.
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