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.
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Pipeline Credit: Google Image |
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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.
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Source: Google Image |
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 |
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