In this episode of DevOpsLab, Abel and Dan discuss the different integration points between Azure Boards and GitHub. 

We will answer the questions of: Why use Azure Boards for planning and GitHub for code? What are the benefits of using them both together and what is the Azure Boards App in GitHub? What problem does it solve? What are the open source actions for and how to use them?

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Create a Free Azure DevOps Account

Here’s a deep dive into understanding how you can leverage individual credits for enterprise work, and drive innovation for lasting impact.  We’ll look at how you can use these subscriptions, their benefits and how you can enable various security scenarios and protocols leveraging Azure AD.

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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.  

Subscribe for more free data analytics videos: https://www.youtube.com/saschadittmann?sub_confirmation=1And don’t forget to click the bell so you don’t miss anything. Share this video with a YouTuber friend: https://youtu.be/mZUdYu345dg

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 [email protected]

✅ Let’s connect:Twitter: https://twitter.com/SaschaDittmannFacebook: https://www.facebook.com/DataDrivenDevInstagram: https://www.instagram.com/saschadittmann/LinkedIn: https://www.linkedin.com/in/saschadittmannGitHub: 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 #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 [email protected]

✅ 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

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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

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