ML Engineering – Scalable Model Serving (FREE)

Date
May 28, 2020
Start Time
4pm PST
End Time
6pm PST

Overview

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

In this session we will discuss  how to do scalable model serving architectures.

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

  • Compare and contrast traditional methods of model serving with Serverless paradigm
  • Serverless inference in the cloud
  • tensorflow model server
  • AWS Lambda
  • Serving a model using TF Model server

Intended Audience

Developers, DevOps, Data Scientists

Prerequisites

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

Coming soon

Session Notes

Github repo

 


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 : https://www.linkedin.com/in/sujeemaniyam
Github : https://github.com/sujee

 

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 : https://www.linkedin.com/in/mikekane2/
Github : https://github.com/mike-kane