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README.md

Machine Learning Operations – Applying DevOps to Data Science

Session Abstract

While many companies have adopted DevOps practices to improve their software delivery, these same techniques are rarely applied to machine learning projects. Collaboration between developers and data scientists can be limited and deploying models to production in a consistent and trustworthy way is often a pipedream.

In this session, you’ll learn how to apply DevOps practices to your machine learning projects using Azure DevOps and Azure Machine Learning Service. We’ll set up automated training, scoring, and storage of versioned models and wrap the models in docker containers and deploy them to Azure Container Instances and Azure Kubernetes Service. We’ll even collect continuous feedback on model behavior so we know when to retrain.

Table of Content

Resources Links
PowerPoint - Presentation
Videos - Dry Run Rehearsal
- Microsoft Ignite Orlando Recording
Demos - Demo 1 - Show Faulty Prediction and Make a Change
- Demo 2 - Build a Pipeline with Jupyter Notebooks
- Demo 3 - Show the Build in Progress
- Demo 4 - Show the Release Process

How To Use

Welcome, Presenter!

We're glad you are here and look forward to your delivery of this amazing content. As an experienced presenter, we know you know HOW to present so this guide will focus on WHAT you need to present. It will provide you a full run-through of the presentation created by the presentation design team.

Along with the video of the presentation, this document will link to all the assets you need to successfully present including PowerPoint slides and demo instructions & code.

  1. Read document in its entirety.
  2. Watch the video presentation
  3. Ask questions of the Lead Presenter

Assets in Train-The-Trainer kit

Become a Trained Presenter

To become a Trained Presenter, contact scalablecontent@microsoft.com. In your email please include:

  • Complete name:
  • The code of this presentation: AIML50
  • Link (ex: unlisted YouTube video) to a video of you presenting (~10 minutes).

It doesn't need to be this content, the important is to show your presenter skills

Trained Presenters

Thanks goes to these wonderful people (emoji key):

Damian Brady
Damian Brady

📢
Steven Murawski
Steven Murawski

📖
Seth Juarez
Seth Juarez

📢
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