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

April 16, 2020
Start Time
End Time


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


Sujee Maniyam

Sujee Maniyam is a seasoned practitioner and founder of Elephant Scale.  He teaches and consults in AI (machine learning and deep learning) and Big Data  (Hadoop, Spark, NoSQL) and and Cloud technologies.  

He is an open source contributor, author ( ‘Hadoop illuminated‘ and ‘HBase Design Patterns‘)  and speaker at conferences.  He also advises and mentors various companies and organizations.

Linkedin :
Github :

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

Linkedin :
Github :