Programming with Mosh provides this Git tutorial for beginners.

TABLE OF CONTENTS

0:00:00 Introduction
0:00:35 What is Git?
0:03:07 Using Git
0:06:11 Installing Git
0:07:38 Configuring Git
0:12:43 Getting Help
0:13:35 Cheat Sheet
0:14:05 Taking Snapshots
0:14:38 Initializing a Repository
0:17:10 Git Workflow
0:21:46 Staging Files
0:25:24 Committing Changes
0:27:37 Committing Best Practices
0:30:21 Skipping the Staging Area
0:31:46 Removing Files
0:33:48 Renaming or Moving Files
0:36:06 Ignoring Files
0:42:41 Short Status
0:45:33 Viewing the Staged and Unstaged Changes
0:50:33 Visual Diff Tools
0:55:27 Viewing the History
0:57:39 Viewing a Commit
1:01:37 Unstaging Files
1:04:28 Discarding Local Changes
1:06:17 Restoring a File to an Earlier Version

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 CloudBlog@gmx.de

✅ 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 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.

The Visual Studio team has spent quite a bit of time recently improving Git integration in Visual Studio 2019.

PMs Pratik Nadagouda and Taysser Gherfal show the latest updates, including the new Git Repository window and Merge Conflict Resolution improvements, as well as additional ease of use features.

For more information, see the blog post at https://aka.ms/newvsgitblog.

GitHub Actions makes it easy to automate all your software workflows.

Tim Heuer joins Scott Hanselman to saunter through the process of deploying .NET Core apps to Azure using GitHub Actions.

Index:

  • [0:00:00]- Overview
  • [0:00:19]- Project setup
  • [0:04:02]- Configuring the workflow
  • [0:07:29]- Build job – setting up the environment
  • [0:13:18]- Build job – configuring the build
  • [0:16:07]- Getting the publish profile from Azure
  • [0:17:45]- Build job – handling secrets
  • [0:20:37]- Build job – deploying to Azure
  • [0:22:34]- Actions tab in GitHub and workflow log review
  • [0:24:59]- Adding artifacts to the job
  • [0:27:59]- Wrap-up

Related links:

How do you incorporate load testing into your DevOps pipeline?

In this episode, Tim Koopmans walks us through the steps of writing a browser-based load testing script using Flood Element, adding it as a task in Azure Pipelines, and ramping it up using Tricentis Flood. Running load tests continuously allows teams to easily spot performance degradation over time.

Jump To

  • [01:39] Overview of Flood platform
  • [02:55] What load testing can show you: Flood analytics
  • [04:44] Aspects of Performance (PEARS): what should you test for?
  • [05:38] Browser-level load testing with Flood Element
  • [06:29] DevOps Starter Pack + Azure Pipelines + Flood
  • [09:02] Writing a load testing script using Flood Element
  • [13:45] Adding load testing execution step to Azure DevOps pipeline using Flood API
  • [16:16] Scaling up load tests or keeping it small for continuous testing
  • [20:07] The value of including load testing in your DevOps pipelines

More Information: