Build and evaluate robust computer vision models
Gain visibility into your data and model performance and curate data that will improve your model most efficiently
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Who we help

DATA CURATION AND MODEL 
performance

For ML teams

  • How can I improve my model?
  • What data do I need to label?
  • How well will a model work on a new dataset?
  • Will new data help my model?
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model understanding and evaluation

For AI adopters

  • How well will a model work on my data?
  • Is the model fair?
  • Is the model robust to different inputs?
  • Why did the model make its decisions?
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using your data to train ai models

For data owners

  • How do I quickly get started building a model?
  • What data should I label first?
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How it works

Visualise trends in large datasets
Data slicing and curation
Results reporting
Quickly spot and analyse data distribution differences, errors, bias and outliers
Understand model performance on a granular level with respect to input data
Stratified reporting of results based on user-provided subsets

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CONTACT
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