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.
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.
- Read document in its entirety.
- Watch the video presentation
- Ask questions of the Lead Presenter
- This guide
- PowerPoint presentation including notes for each slide here
- Full-length recording of presentation here
- Director Cut Full-length recording of presentation here
- Individual recordings of stage-ready hands-on demos here
- Demo Guide here
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It doesn't need to be this content, the important is to show your presenter skills
Thanks goes to these wonderful people (emoji key):
 Damian Brady 📢 |
 Steven Murawski 📖 |