Machine Learning Operations – Applying DevOps to Data Science
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
|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
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Assets in Train-The-Trainer kit
- This guide
- PowerPoint presentation including notes for each slide here or presentations.md
- Session at Microsoft Ignite 2019 Orlando here
- Full-length recording of presentation here
- Individual recordings of stage-ready hands-on demos
- Demo Guides
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