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Airflow

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1.8K
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107
MLflow

110
350
+ 1
6
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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.

Advice on Airflow and MLflow
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on
Apache Spark
Luigi
and
Airflow

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 93.3K views
Recommends
Cassandra

For a non-streaming approach:

You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.

Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation

Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing

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Pros of Airflow
Pros of MLflow
  • 42
    Features
  • 13
    Task Dependency Management
  • 12
    Beautiful UI
  • 10
    Cluster of workers
  • 9
    Extensibility
  • 5
    Open source
  • 4
    Complex workflows
  • 4
    Python
  • 2
    Custom operators
  • 2
    K
  • 2
    Dashboard
  • 1
    Good api
  • 1
    Apache project
  • 3
    Code First
  • 3
    Simplified Logging

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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 MLflow?

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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What companies use MLflow?
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What tools integrate with Airflow?
What tools integrate with MLflow?
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.
Pachyderm
Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
See all alternatives
How developers use Airflow and MLflow
Eugene Ivanchenko uses
Airflow

Manage the calculation pipeline and data distribution procedures.

Christopher Davison uses
Airflow

Used for scheduling ETL jobs