Machine Learning Engineering – Speeding up training using GPU & TPU (FREE)

April 16, 2020
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This is part of Machine Learning Engineering and DevOps Learning Series.

In this series we will demonstrate a neural network  to process images (CIFAR).  Using CPUs the training takes a long time to run (~10 mins).   We will show how how switching to GPU cuts down the training time to just few seconds (under a min).

Also we will show how to utilize TPU system

This is a FREE class!


Can’t make it to live session?  No worries.  Go ahead and register; we will send you the session recording .
See below for past session recordings & notes

What you will learn

  • Sample TF2 program to process CIFAR image dataset
  • Using free GPU/TPU on Google Colab
  • Speeding up training by using GPU
  • How to use TPU

Intended Audience

Developers, DevOps, Data Scientists


  • Must have : Development experience
  • Nice to have: Python knowledge

What to Bring

  • Please bring a reasonably modern laptop (Corporate laptops with overly restrictive firewalls may not work well;  Personal laptops are recommended)
  • Need to have a Machine Learning Environment setup on your laptop.  Please follow this guide
  • [nice to have] download our docker image elephantscale/es-training

Session Recording

Session Notes

Github repo


Mike Kane

Mike Kane is a Senior Data Scientist specializing in Natural Language Processing. He has a passion for Deep Learning, and has professionally trained teams from multiple Fortune 100 companies in ML and AI

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