Custom Workflows

Customize Prodigy for your specific use case
and implement powerful automated workflows,
interfaces and integrations.

 recipe.py
@prodigy.recipe(
    "my_custom_recipe"
    dataset=Arg(help="dataset to save answers to"),
    source=Arg("--source", help="data to load"),
    label=Arg("--label", "-l", help="comma-separated label(s)"),
)
def recipe(dataset: str, source: str, dataset: List[str]):
    ...

Terminal

prodigymy_custom_recipeannotations./samples.jsonl--label PERSON,PRODUCT

Built for customization and extension

Prodigy lets you implement entirely custom automated workflows and integrations with your existing stack, internal resources and tools, and the large open-source machine learning and data science ecosystem. If you can use it in Python, you can use it with Prodigy!

Interfaces can be flexibly combined to fit your project’s needs and even extended with your own HTML, CSS and JavaScript for fully interactive custom experiences.

 script.js
 styles.css
 recipe.py
@prodigy.recipe(
    "review_teasers"
    dataset=Arg(help="Dataset to save answers to"),
    source=Arg("--source", help="Data to load"),
This live demo requires JavaScript to be enabled.

Make your annotation as efficient as possible

The way you organize your data can have a huge impact on annotation efficiency. If your annotators constantly have to swap tasks and think about lots of things at once, the work will be both slower and less accurate. With Prodigy’s customizable data feed and interface, you can create workflows that have your annotators flying.

Documentation

Overview
  • Downloadable developer tool and library
  • Create, review and train from your annotations
  • Runs entirely on your own machines
  • Powerful built-in workflows

Pricing

Overview
  • Lifetime license, pay once, use forever
  • Flexible options for individuals and teams
  • Full privacy, no data leaves your servers
  • Download and install like any other library