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We understand that while there are a lot of machine learning frameworks and libraries available, putting the models in production at large scale is still a challenge. I’d like to talk about how we took on the challenge of deploying deep learning models in production: how we chose our tools and developed our internal deep learning infrastructure. I’ll cover how we do model training in Docker containers, distributed TensorFlow training in a cluster of containers, automated re-training of models and finally - deployment of models using Kubernetes. I’ll also talk about how we optimize our model prediction infrastructure for latency or throughput depending on the use case.
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