Machine Learning Engineering – Monitoring training (FREE)

Date
April 23, 2020
Start Time
16:00:00
End Time
18:00:00

Overview

This is part of Machine Learning Engineering and DevOps Learning Series.

Here we will focus on how to monitor training and be notified when training is done

This is a FREE Class!

On-Demand

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

  • Using callbacks and TensorBoard to monitor training progress
  • Set model to train until to a point (90% accuracy) using callbacks instead of fixed set # of epochs
  • Send periodic summary statistics using Slack and/or Twilio
  • Notify MLE when training is done!

Intended Audience

Developers, DevOps, Data Scientists

Prerequisites

  • 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

Presenters

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


Session Recording


Session Notes

Github repo