Presentation: Tools to Put Deep Learning Models in Production

Track: The Practice & Frontiers of AI

Location: Mountbatten, 6th flr.

Duration: 11:50am - 12:40pm

Day of week: Tuesday

Level: Advanced

Share this on:


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: Sahil Dua

Developer at; Open Source Contributor in DuckDuckGo, GitHub and Pandas

Sahil is a software developer at He has been involved in leveraging container infrastructure to help’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 DuckDuckGo's open source organisation. Besides that he is one of the contributors to the Git project, pandas - open source data analysis library, Linguist project by GitHub and Go-GitHub project by Google.

Find Sahil Dua at

Similar Talks

CEO @Skipjaq & Co-Founder of SpringSource
Principal Solutions Architect @SAP
Engineer @Redhat working on CRI-O Container Runtime
Principal Engineer @Intel Open Source Technology Center
Cloud Technology Consultant with an expertise in Serverless Computing