Human-in-the-Loop functions
At Dataloop, we know that AI is dependent on human input for feeding models with validated data. Using our human-in-the-loop plugins, you can create powerful deep learning pipelines that require minimal manual intervention, while ensuring maximum model accuracy:
Dataset production
Combine active learning with model integration to accelerate dataset preparation, as models pre-annotate items prior to manual labeling. Annotators then only audit pre-labeled data instead of labeling from scratch
Data auditing
Turn the annotation process into a model auditing task - run models on your data to receive visual representations of object recognition. Annotators then audit model output by fixing errors and identifying false negatives / positives
Setting thresholds
Set confidence thresholds for inferencing, sending only items with below-threshold accuracy to be annotated by humans. As models improve, manual labeling is applied only to edge-cases and random validation samples
Labeling in production
Close the image recognition loop even after reaching deployment in production, by sending items post-inferencing to be validated, fixed and re-applied by human annotators
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Model Serving
Run any model or logic
Pre/Post Data Processing
Enhance and refine your data
Human-in-the-loop
Weave human and machine functions
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Data
Management
Manage, collaborate, distribute and utilize your data operations, all integrated seamlessly and managed via single point of access.
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Production
Pipelines
Build custom automation pipelines within our serverless environment, to reach production faster and scale indefinitely.
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