Our ML Tooling 2023 - Part 3: Modeling

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Fig 1. Our updated ML Tooling diagram, zoomed in on the Model section.

Fig 2. Check out this Tensorflow tutorial, served from GitHub pages and built with Jupyter Book.

  • 1Run unique and informative experiments

  • 2Reproduce those same experiments, including producing evaluation metrics and model artifacts

  • 3Store model artifacts and record their versions for internal use and comparison

  • 4Serve models in performant applications that offer easy deployment and flexibility for models with various compute resource requirements and detection types

What we're doing.

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