Setup Azure Machine Learning Services (Azure MLOps Part 2)

Setup Azure Machine Learning Services (Azure MLOps Part 2)

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

Getting Started with MLOps with Azure DevOps

Getting Started with MLOps with Azure DevOps

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.

Get Started with Reinforcement Learning on Azure Machine Learning

Get Started with Reinforcement Learning on Azure Machine Learning

Are you curious how data scientists and researchers train agents that make decisions? 

Learn how to use reinforcement learning to optimize decision making using Azure Machine Learning.  We show you how to get started.

Time Index:

  • [00:36] – What is reinforcement learning?
  • [01:37] – How do reinforcement learning algorithms work?
  • [04:10] – Reinforcement Learning on Azure – Notebook sample
  • [05:17] – Reinforcement Learning Estimator
  • [07:21] – Sample training Python script
  • [09:06] – Training Result
  • [10:15] – What kind of problems can you solve with reinforcement learning?

Learn More:

The AI Show’s Favorite links:

Time Series Forecasting with Automated Machine Learning

Time Series Forecasting with Automated Machine Learning

Building forecasts is an integral part of any business, whether it’s revenue, inventory, sales, or customer demand.

Building machine learning models can be a time-consuming and complex with many factors to consider, such as iterating through algorithms, tuning your hyperparameters and feature engineering.

These choices multiply with time series data, with additional considerations of trends, seasonality, holidays and effectively splitting training data.

Forecasting within automated machine learning (ML) takes these factors into consideration and includes capabilities that improve the accuracy and performance of our recommended models.

This session will highlight the forecasting features of Automated ML and how to leverage them.

Time index:

  • [00:35] – What is time-series forecasting?
  • [01:30] – Simplify ML with Automated ML
  • [02:30] – DriveTime customer scenario
  • [04:15] – Features & Functionality
  • [05:20] – Demo

Learn More:

The AI Show’s Favorite links:

Optimization, Machine Learning Models, and TensorFlow

Optimization, Machine Learning Models, and TensorFlow

This is Part 2 of a four-part series that breaks up a talk that Seth Juarez gave at the Toronto AI Meetup. (Watch Part 1)

Index:

  • [00:13] Optimization (I explain calculus!!!)
  • [04:40] Gradient descent
  • [06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more)
  • [07:04] Neural Networks (as an extension to linear models)
  • [09:28] Brief Review of TensorFlow