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<p class="light">Write once and run anywhere. Develop your model using DJL and run it on an
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<h5>“The Netflix observability team's future plans with DJL include trying out its training
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<h5 class="light grey-text text-lighten-3">Stanislav Kirdey, Engineer at Netflix
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<h5>“Using DJL allowed us to run large batch inference on Spark for Pytorch models. DJL
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<h5>“DJL enables us to run models built with different ML frameworks side by side in the
same JVM without infrastructure changes. ”</h5>
<h5 class="light grey-text text-lighten-3">-- Hermann Burgmeier, Engineer at Amazon
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<h5>“Our science team prefers using Python. Our engineering team prefers using Java/Scala.
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Tensorflow, Pytorch, and MXNet, and engineering team can run inference on these models
using DJL. We found that our batch inference time was reduced by 85% from using DJL.”
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<h5 class="light grey-text text-lighten-3">-- Vaibhav Goel, Engineer at Amazon Behavior
Analytics team</h5>
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