Machine Learning Engineering – Model Serving (FREE)

Date
May 14, 2020
Start Time
16:00:00
End Time
18:00:00

Overview

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

In this session we will discuss  how to serve models

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

  • Prepping the model for serving
  • Inference architecture
  • Simple model serving using Python web service
  • Model servers: Tensorflow Model Server, AWS Sagemaker
  • Serving a model using Tensorflow Model Server
  • Inference load balancing and best practices

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

 

Session Recording

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