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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