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.

Powerpoint Designer utilizes machine learning to provide users with redesigned slides to maximize their engagement and visual appeal.

Up to 4.1 million Designer slides are created daily and the Designer team is adding new types of content continuously.

Time Index:

  • [02:39] Demo – PowerPoint suggests design ideas to help users build memorable slides effortlessly
  • [03:28] A behind-the-scenes look at how PowerPoint was built to make intelligent design recommendations
  • [04:47] AI focused on intelligently cropping images in photos and centering the objects, positioning the images, and even using multi-label classifiers to determine the best treatment.
  • [06:00] How PowerPoint is solving for Natural Language Processing (NLP).
  • [07:32] Providing recommendations when image choices don’t meet the users’ needs.
  • [09:30] How Azure Machine Learning helps the dev team scale and increase throughput for data scientists.
  • [11:10] How distributed GPUs helps the team work more quickly and run multiple models at once.

Azure Synapse is a limitless analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. Azure Synapse brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs. All of this leverages our limitless Azure Data Lake Storage service for any type of data.

Microsoft Mechanics explains.

Azure machine learning datasets is a great solution to manage your data for machine learning.

With datasets, you can directly access data from multiple sources without incurring extra storage cost; load data for training and inference through unified interface and built in support for open source libraries; track your data in ML experiments for reproducibility.

Learn More:

Azure Machine Learning has a large library of algorithms from the classification, recommender systems, clustering, anomaly detection, regression and text analytics families.

Each is designed to address a different type of machine learning problem.

In this demo, you will learn how to use Azure Machine Learning designer in a few simple steps and create an end-to-end machine learning pipeline for your data science scenario.

Additional information:

Azure Machine Learning compute instances (formerly Notebook VMs) is a hosted PaaS offering that supports the full lifecycle of inner-loop ML development–from model authoring, to model training and model deployment.

AzureML Compute Instances are deeply integrated with AzureML workspaces and provide a first-class experience for model authoring through integrated notebooks using AzureML Python and R SDK.

Learn More:

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