view our previous webinars

data augmentation

A Data-Centric Approach to Augmentation

Learn about the role augmentation can play in building large, robust computer vision datasets. We’ll discuss different augmentation techniques, when and how to apply them, and finish with a case study that focuses on using augmentation to improve the accuracy of YOLO on a popular class.

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Winning Approaches to Andrew Ng’s Data-Centric AI Competition

Watch as we discuss common problems and informed solutions to dataset curation and model-centric development, and reveal how we applied those techniques to place second in Andrew Ng’s Data-Centric AI Challenge. Other topics include:

  • Analysis of errors in benchmark datasets and their impact
  • Growing the data-centric AI movement
  • Key metrics for high-quality datasets
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The 3 Major Components to Build a Machine Learning Dataset

Watch as we discuss three major components required to build robust datasets used to train high performing Machine Learning models, including:

  • Fast And Accurate Annotation Capabilities
  • Insightful Analytics
  • Seamless Team Management
  • Even More Advanced Methods of Building Quality Datasets
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A Data-Centric Approach to Implementing MLOps

Watch as we discuss a data-centric approach to implementing MLOps in your Machine Learning pipeline, including:

  • Annotation QA and review processes
  • Utilizing pre-annotation and imports
  • Using metadata to refine existing annotations
  • Visualizing and rebalancing datasets
  • Integrating an API

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