Task Orchestration Tool and Their Comparison

Writen by Chai Biscuit 1:15 PM - 0 Comments

 What is Task Orchestration Tool

Cleaning data, training machine learning models, monitoring performance, and deploying the models to a production server are common tasks for smaller teams to begin with. The number of repetitive steps increases as the team and solution expand in size. It becomes much more important that these activities are completed in a timely manner.

The degree to which these activities are interdependent grows as well. You will have a pipeline of activities that need to be run once a week or once a month when you first start out. These tasks must be completed in the correct order. This pipeline evolves into a network of dynamic branches as you expand. In several cases, some tasks trigger the execution of others, which may be dependent on the completion of some other tasks first.

This network can be represented as a DAG (Directed Acyclic Graph), which represents each task and its interdependencies.

Pipeline      Credit: Google Image

DAG   Credit:Google Image

There has been a recent proliferation of new tools for orchestrating task- and data workflows (also known as "MLOps"). Since the sheer number of these tools makes it difficult to determine which to use and how they interact, we decided to pit some of the most common against one another.

Source: Google Image

It is clear that the most common solution is airflow, followed by Luigi. There are also newer candidates, all of whom are rapidly expanding.

Comparison Table

	Maturity	Popularity	Simplicity	Breadth	Language
Apache Airflow	B	A	C	A	Python
Luigi	B	A	A	B	Python
Argo	C	B	B	B	YAML
Kubeflow	C	B	B	C	Python
MLFlow	C	B	A	C	Python

While each of these techniques has its own set of strengths and weaknesses, none of them can guarantee a pain-free procedure right out of the box. Before you start worrying about the tool to use, make sure you have strong processes in place, such as positive team culture, blame-free retrospectives, and long-term goals.

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