Full Stack Deep Learning
  • Full Stack Deep Learning
  • Course Content
    • Setting up Machine Learning Projects
      • Overview
      • Lifecycle
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    • Infrastructure and Tooling
      • Overview
      • Software Engineering
      • Computing and GPUs
      • Resource Management
      • Frameworks and Distributed Training
      • Experiment Management
      • Hyperparameter Tuning
      • All-in-one Solutions
    • Data Management
      • Overview
      • Sources
      • Labeling
      • Storage
      • Versioning
      • Processing
    • Machine Learning Teams
      • Overview
      • Roles
      • Team Structure
      • Managing Projects
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    • Training and Debugging
      • Overview
      • Start Simple
      • Debug
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      • Conclusion
    • Testing and Deployment
      • Project Structure
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      • Docker
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    • Research Areas
    • Labs
    • Where to go next
  • Guest Lectures
    • Xavier Amatriain (Curai)
    • Chip Huyen (Snorkel)
    • Lukas Biewald (Weights & Biases)
    • Jeremy Howard (Fast.ai)
    • Richard Socher (Salesforce)
    • Raquel Urtasun (Uber ATG)
    • Yangqing Jia (Alibaba)
    • Andrej Karpathy (Tesla)
    • Jai Ranganathan (KeepTruckin)
    • Franziska Bell (Toyota Research)
  • Corporate Training and Certification
    • Corporate Training
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  1. Course Content
  2. Data Management

Labeling

What are effective ways to label your data?

PreviousSourcesNextStorage

Last updated 5 years ago

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Summary

  • Data labeling requires a collection of data points such as images, text, or audio and a qualified team of people to label each of the input points with meaningful information that will be used to train a machine learning model.

  • You can create a user interface with a standard set of features (bounding boxes, segmentation, key points, cuboids, set of applicable classes…) and train your own annotators to label the data.

  • You can leverage other labor sources by either hiring your own annotators or crowdsourcing the annotators.

  • You can also consult standalone service companies. Data labeling requires separate software stack, temporary labor, and quality assurance; so it makes sense to outsource.

Labeling - Data Management