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.
Many Dash apps need to run scheduled or long-running jobs. For example, a Dash app might need to poll a remote API every 5 minutes, send an email report every night at midnight, or retrain an 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.
This Dash app runs an ML model as a background task on the Dash Enterprise Job Queue. When the job has finished running, it automatically generates and archives an interactive report with Snapshot Engine that displays common metrics and graphs (ROC Curve, PR Curve, etc).
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