Machine Learning DevOps and A/B testing using docker and python

Training a machine learning / deep learning model is one thing and deploying it to a production is completely different beast. Not only you have to deploy it to a production, but you will have to retrain the model every now and then and redeploy the updates. With many machine learning / deep learning projects / POCs running in parallel with multiple environments such as dev, test prod, managing model life cycle from training to deployment can quickly become overwhelming. In this talk, I will discuss an approach to handle this complexity using Docker and Python. Rough outline of the talk is,

  • Introduction to the topic
  • Problem statement
  • Quick introduction to Docker
  • Discussing the proposed architecture
  • Alternative architecture using AWS infrastructure
  • Demo
 
 

Outline/Structure of the Talk

Rough outline of the talk is,

  • Introduction to the topic
  • Problem statement
  • Quick introduction to Docker
  • Discussing the proposed architecture
  • Alternative architecture using AWS infrastructure
  • Demo

Learning Outcome

Seamlessly manage machine learning model pipeline using Docker and Python

Target Audience

Developers, DevOps engineers

Prerequisites for Attendees

Basics of Docker and Python

schedule Submitted 1 year ago

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