This network can be modelled as a DAG – a Directed Acyclic Graph, which models each task and the dependencies between them.Ī pipeline is a limited DAG where each task has one upstream and one downstream dependency at most. In certain cases, some tasks set off other tasks, and these might depend on several other tasks running first. As you grow, this pipeline becomes a network with dynamic branches.
These tasks need to be run in a specific order. When you start out, you might have a pipeline of tasks that needs to be run once a week, or once a month. The complex ways these tasks depend on each other also increases. It also becomes more important that these tasks are executed reliably. As the size of the team and the solution grows, so does the number of repetitive steps. Smaller teams usually start out by managing tasks manually – such as cleaning data, training machine learning models, tracking results, and deploying the models to a production server. What is task orchestration and why is it useful? Argo is the one teams often turn to when they’re already using Kubernetes, and Kubeflow and MLFlow serve more niche requirements related to deploying machine learning models and tracking experiments.īefore we dive into a detailed comparison, it’s useful to understand some broader concepts related to task orchestration. Overall Apache Airflow is both the most popular tool and also the one with the broadest range of features, but Luigi is a similar tool that’s simpler to get started with. The quantity of these tools can make it hard to choose which ones to use and to understand how they overlap, so we decided to compare some of the most popular ones head to head. Recently there’s been an explosion of new tools for orchestrating task- and data workflows (sometimes referred to as “MLOps”). ( source) Task orchestration tools and workflows There are newer contenders too, and they’re all growing fast. Airflow is the most popular solution, followed by Luigi.
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