view our previous webinars

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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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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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