Azure Machine Learning Deployment Workflow

Francesca Lazzeri
Oct 12, 2018 · 4 min read

This blog post is co-authored by Jaya Mathew and Francesca Lazzeri, data scientists at Microsoft.

The Artificial Intelligence Conference in London is a relatively addition to the list of conferences hosted by O’Reilly worldwide. The aim of this conference is to create a forum for the ever-growing AI community to explore the most essential issues and innovations in applied AI. In the conference the various talks covered topics ranging from practical business applications of AI, to compelling AI enabled use cases, to various technical trainings and deep dive into successful AI projects etc.

In our session “A day in the life of a data scientist in an AI company”, we presented a scientific framework to help organizations to systematically discover opportunities to create value from data, qualify new opportunities and assess their fit and potential, then how to build a team to smoothly implement end-to-end advanced analytics pilots and projects, and produce sustainable ongoing business value from data. Specifically we shared a few important concepts, such as the Machine Learning Workflow and the Team Workspace.

In the session, we also introduced the audience to Azure AI’s latest offering Azure Machine Learning Service. Azure Machine Learning Service (Preview) is a cloud service that you can use to develop and deploy machine learning models. Using Azure Machine Learning Service, you can track your models as you build, train, deploy, and manage them, all at the broad scale that the cloud provides.

The Machine Learning Workflow

The goal is to help companies fully realize the benefits of their analytics program. The life cycle outlines the major stages that projects typically execute, often iteratively:

  • Business Understanding
  • Data Acquisition and Understanding
  • Modeling
  • Deployment
  • Customer Acceptance and Consumption

The following diagram provides a view of the tasks (in blue) associated with each stage of the life cycle:

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The Team Workspace

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You can use Python to get started with Azure Machine Learning. In the snippet above, we are creating a workspace called “Demo” in the resource group “Contoso” which resides in the given subscription. The workspace will be created in the Azure region “eastUS2”.

You can create multiple workspaces, and each workspace can be shared by multiple people. When sharing a workspace, control access to the workspace by assigning the following roles to users:

  • Owner
  • Contributor
  • Reader

When you create a new workspace, it automatically creates several Azure resources that are used by the workspace:

Azure Machine Learning Deployment Workflow

The following diagram illustrates the complete deployment workflow:

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In the next few paragraphs, we will show how to perform the following steps:

  1. Register the model in a registry hosted in your Azure Machine Learning Service workspace
  2. Register an image that pairs a model with a scoring script and dependencies in a portable container
  3. Deploy the image as a web service in the cloud or to edge devices

Step 1: Register model

Step 2: Register image

Step 3: Deploy image

Conclusion

References

Microsoft Azure

Any language.

Francesca Lazzeri

Written by

Senior Machine Learning Scientist Lead at Microsoft — Previously Harvard — PhD — Find her @frlazzeri on Twitter.

Microsoft Azure

Any language. Any platform. Our team is focused on making the world more amazing for developers and IT operations communities with the best that Microsoft Azure can provide. If you want to contribute in this journey with us, contact us at medium@microsoft.com

Francesca Lazzeri

Written by

Senior Machine Learning Scientist Lead at Microsoft — Previously Harvard — PhD — Find her @frlazzeri on Twitter.

Microsoft Azure

Any language. Any platform. Our team is focused on making the world more amazing for developers and IT operations communities with the best that Microsoft Azure can provide. If you want to contribute in this journey with us, contact us at medium@microsoft.com

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