Sascha Dittmann has created a series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, it’s here on Frank’s World and on YouTube.  

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Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used: https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps: https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service: https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension: https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

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DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureMLIn this series of videos I’m showing how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure.

In the second video of this 5-part series, you’ll discover how to connect Azure DevOps to your Azure Subscription, as well as create and configure Azure Machine Learning Services from your DevOps pipeline.

If you haven’t yet seen the first video in this series, I strongly recommend that you do so:

Subscribe for more free data analytics videos:
https://www.youtube.com/saschadittmann?sub_confirmation=1
And don’t forget to click the bell so you don’t miss anything.

Share this video with a YouTuber friend:

If you enjoyed this video help others enjoy it by adding captions in your native language:
https://www.youtube.com/timedtext_video?v=mZUdYu345dg

Watch my most recent upload: http://bit.ly/2OihAlj

Recommended links to learn more about DevOps for Machine Learning (MLOps):

The GitHub repo with the example code I used:
https://github.com/SaschaDittmann/MLOps-Lab

Azure DevOps:
https://azure.microsoft.com/en-us/services/devops/

Azure Machine Learning Service:
https://azure.microsoft.com/en-us/services/machine-learning-service/

Azure Machine Learning CLI Extension:
https://docs.microsoft.com/en-us/azure/machine-learning/service/reference-azure-machine-learning-cli

✅ For business inquiries contact me at CloudBlog@gmx.de

✅ Let’s connect:
Twitter: https://twitter.com/SaschaDittmann
Facebook: https://www.facebook.com/DataDrivenDev
Instagram: https://www.instagram.com/saschadittmann/
LinkedIn: https://www.linkedin.com/in/saschadittmann
GitHub: https://github.com/SaschaDittmann

DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This helps support my channel and allows me to continue making awesome videos like this. Thank you for the support!

#MLOps #DevOpsForMachineLearning #AzureML

Sascha Dittmann shows us how to get started with DevOps for Machine Learning (MLOps) on Microsoft Azure in this first in a series of videos.

In the first video of this 5-part series, you’ll discover how to create an Azure DevOps project, import sample machine learning code and create a DevOps pipeline to process simple Data Quality Checks.I use services like Azure DevOps and Azure Machine Learning Services for this challenge.

Maintaining both quality and speed is a real challenge, and testing methodologies can either aid or downshift the acceleration rapid application development.

See how using right tools, continuous integration can address these challenges so that testing is no longer a burden that rests solely on the shoulders of the test team; but is an integral part of the product development from the start of development cycle all the way to release.

Time index:

  • [04:24] Real Device Testing
  • [05:50] Adhoc Testing with Real Devices
  • [10:06] Augmented Manual Testing
  • [14:40] “No code” Automated Testing
  • [20:41] Smart Automation Recording
  • [22:35] Azure DevOps integration

More Information:

DevOps Lab Favorite Links:

GitHub Actions lets you take code in your GitHub repository and add automation around it. 

You can create workflows that respond to issue comments, handle pull requests, or perform CI/CD on macOS, Windows and Linux. 

It’s easy to create workflows that build your code to validate pull requests or deploy it when you create a release.

Time Index:

  • [01:25] – What is GitHub Actions?
  • [03:50] – Demo: Getting started with GitHub Actions
  • [08:38] – Demo: add a deployment workflow
  • [12:26] – Extending GitHub Actions

Learn More:

Sean Ferguson joins Abel Wang to chat about a new added Deployment control that integrates work items with Releases.

With this control, you can track where and when your completed work item is being deployed. All from the work itself.

Time Index:

  • [00:34] – Context of the Deployments Control
  • [01:18] – Pipeline Configuration
  • [02:26] – Make a code change, link to work item, and commit
  • [03:06] – Start the release
  • [03:25] – Deployment control to view stages
  • [05:07] – Recap and summary

Learn More:

In this episode of Visual Studio Toolbox, Robert is joined by Brian Randall, who shows us the new Pull Request experience in Azure DevOps Services.

Brian reviews the process of creating and approving pull requests and highlights the new and simpler pull requests UI. The new experience is mobile-friendly and faster, and includes several new features that help you review pull requests quicker and improve your overall pull request experience.

To learn more, see the Introducing the New Pull Request Experience for Azure Repos blog post.

How can you deploy applications to Azure Platform-as-a-Service (PaaS) like Azure Kubernetes Service (AKS) without having any downtime? How can you automate this, and how can you ensure my customers won’t notice changes are happening?

In this interview, you can see Damian chat with Marcel de Vries about how to use Azure DevOps Build and Release pipelines to deliver your application to production many times a day while not interrupting your users.     

Time Index:
  • [00:57] – What does PaaS mean
  • [02:00] – Defining Zero Downtime
  • [03:21] What is involved to get to Zero Downtime
  • [04:36] Introduction to demo using MVC Music Store on AKS & Windows containers
  • [07:22] Making the change we want to move to production
  • [08:19] The build Pipeline creating the containers
  • [09:15] Azure container registry
  • [10:04] Release Pipeline to AKS
  • [10:54] Explaining the Kubernetes Deployment Description for Zero Downtime Deployment
  • [13:14] Explaining Health Endpoints to guard the deployment
  • [17:35] Kicking off the deployment
  • [18:41] First pods getting updated
  • [20:31] New website visible
  • [20:50] Looking at the telemetry
  • [21:42] Using Feature flags

Free DevOps courses on Microsoft Learn:

DevOps Labs Favorite Links: