Putting Deep Learning into Production

Deep learning models are achieving state-of-the-art results in speech, image/video classification and numerous other areas, but deploying them to production often involves a unique set of challenges including prediction latency, significant training cost, device memory requirements, etc.

This conference will focus on some best practices for deploying deep learning models into production.  Speakers will discuss topics like:

  • Ways to speed up training time
  • Using pre-trained models
  • Transferring knowledge from a different task
  • Reducing model size to improve prediction latency
  • Fitting models onto devices


Jan 21, 2017, 9:30a - 5p



Capital One
201 3rd St, 5th Floor
San Francisco

795.00 995.00

Topics Covered

Increasing training speed

TensorFlow APIs

Model Zoo

Reusing pre-trained models

Reducing model size

Transfer learning across tasks


Sponsors and Media Partners