Scalable, Distributed Deep Learning with Python* and Pachyderm* Webinar

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The recent advances in machine learning and artificial intelligence are amazing! Yet, in order to have real value within a company, data scientists must be able to get their models off of their laptops and deployed within a company’s data pipelines and infrastructure. Those models must also scale to production size data.

In this webinar, we will implement a deep learning model locally using Intel® Nervana™ Neon™ framework. We will then take that model and deploy both it's training and inference in a scalable manner to a production cluster with Pachyderm*. We will also learn how to update the production model online, track changes in our model and data, and explore our results.

What you can expect to learn:

  • How to build a distributed, containerized data pipeline.
  • How to version data.
  • How to track the provenance of results.
  • How to manage DL models in production.

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About Our Speaker

Daniel Whitenack

Data Scientist

Daniel Whitenack (@dwhitena) is a Ph.D. trained data scientist working with Pachyderm (@pachydermIO). Daniel develops innovative, distributed data pipelines which include predictive models, data visualizations, statistical analyses, and more. He has spoken at conferences around the world (ODSC, R Conference NYC, PyCon, GopherCon, and more), teaches data science/engineering with Ardan Labs (@ardanlabs), maintains the Go kernel for Jupyter, and is actively helping to organize contributions to various open source data science projects.