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Airflow
Airflow

343
278
+ 1
19
MLflow
MLflow

12
23
+ 1
0
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Airflow vs MLflow: What are the differences?

Airflow: A platform to programmaticaly author, schedule and monitor data pipelines, by Airbnb. 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; MLflow: An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

Airflow and MLflow are primarily classified as "Workflow Manager" and "Machine Learning" tools respectively.

Some of the features offered by Airflow are:

  • Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.
  • Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
  • Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.

On the other hand, MLflow provides the following key features:

  • Track experiments to record and compare parameters and results
  • Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production
  • Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms

Airflow and MLflow are both open source tools. It seems that Airflow with 13.3K GitHub stars and 4.91K forks on GitHub has more adoption than MLflow with 30 GitHub stars and 13 GitHub forks.

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 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 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.
        Apache Beam
        It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.
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        How developers use Airflow and MLflow
        Avatar of Eugene Ivanchenko
        Eugene Ivanchenko uses AirflowAirflow

        Manage the calculation pipeline and data distribution procedures.

        Avatar of Christopher Davison
        Christopher Davison uses AirflowAirflow

        Used for scheduling ETL jobs

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