Deploying Optimized Deep Learning Pipelines


Optimizing Deep Learning Pipelines with new tools such as quantization, model distillation, and just including the right math library can be complicated for end users. This talk gives a survey on the different techniques for optimizing ML pipelines and lays out trade offs to consider when deploying them.


Adam is the CTO of Konduit. Before this, Adam was the cofounder of Skymind. Adam has been using open and producing open source software since 2010 and has been developing machine learning systems since 2012. Adam is a published author and speaker on the field of deep learning on topics ranging from deployment of Production Machine Learning Systems to NLP. Adam grew up in Michigan in the US, spent a few years in Silicon Valley and now resides in Tokyo, Japan.

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