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Learn MorePros of Airflow
Pros of Kubeflow
Pros of MLflow
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of Kubeflow
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
- Azure0
Pros of MLflow
- Code First5
- Simplified Logging4
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Cons of Airflow
Cons of Kubeflow
Cons of MLflow
Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of Kubeflow
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Cons of MLflow
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What is Airflow?
Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.
What is Kubeflow?
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
What is MLflow?
MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
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What tools integrate with Airflow?
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What are some alternatives to Airflow, Kubeflow, and MLflow?
Luigi
It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
Apache NiFi
An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
Jenkins
In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
AWS Step Functions
AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.