Job Queue

Queueing is key to building scalable ML and AI apps.

Background jobs can dramatically improve the scalability of a Dash app by enabling it to offload slow or CPU-intensive tasks from its callback loops. This helps ensure that the Dash front end can handle incoming web requests promptly, reducing the likelihood of performance issues that occur when requests become backlogged.

The Dash Enterprise Job Queue makes all of this seamless and scalable in Python, R, or Julia. Combine Job Queue with Snapshot Engine to email a PDF or Dash app link when the job is done.

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You'll need a Job Queue
Running ML & AI models
Kubernetes scaling
Production grade
Intuitive API

You'll need a Job Queue.

Many Dash apps need to run scheduled or long-running jobs. Here are a few examples of Python tasks that easily tie into the Dash Enterprise Job Queue:

  • Polling a remote API every 5 minutes
  • Sending an email report every night at midnight
  • Retraining a long-running ML model based on user input

The cron tool is commonly used for this use case, but it is ill-suited for data scientists and horizontally scalable systems like Dash Enterprise. A more powerful, flexible solution is a job scheduler like the Dash Enterprise Job Queue.

80% of Dash Enterprise customers use Job Queues when they bring their Dash applications to production.

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