SESSION + Live Q&A
Tools to Put Deep Learning Models in Production
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 supporting the data scientists with their efforts by making it easy to put their models in production. I’ll be covering how we:
- chose our tools and developed the internal deep learning infrastructure
- train our models in docker containers
- automate the re-training of models
- deploy models using Kubernetes
I’ll also talk about how we optimize our model prediction infrastructure for latency or throughput depending on the use case.
Speaker
![](https://archive.qconlondon.com/sites/qcon_london/files/styles/240x240/public/sahil-dua-bw_0.jpg?itok=777KsHMM)
Sahil Dua
Developer at Booking.com; Open Source Contributor in DuckDuckGo, GitHub and Pandas
Sahil is a software developer at Booking.com. He has been involved in leveraging container infrastructure to help Booking.com’s internal teams in taking advantage of deep learning techniques at scale. An open source software enthusiast, Sahil is a core contributor and community leader for...
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