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· 3 min read
Beatriz Stollnitz

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🗓️ Day 16 of #30DaysOfAzureAI​

Train and Deploy ML Models at Scale

Last week was for Azure AI App developers. This week, we switch gears and focus on Azure Machine Learning services for people building, deploying, and "productionalizing" ML models. If you're a Data Scientist, or an ML or MLOps engineer, then this week is for you.

Today, learn about training and deploying machine learning models using Azure ML.

🎯 What we'll cover​

  • How to train and deploy a machine learning model using Azure ML
  • The three most common methods of creating resources: Azure ML CLI, Python SDK, and Studio UI

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📚 References​

🚌 How to train and deploy in Azure ML​

Read today's article is perfect if you have a basic understanding of how to train a machine learning model, but you've never used Azure ML before, then you're in the right place. Today's article is a hands-on introduction to the most fundamental operations of Azure ML: training and deploying a machine learning model in the cloud. It discusses which resources you need to create, and the three main methods of creating them: the Azure ML CLI, the Python SDK, and the Studio UI.

The goal for today is for you to have a deep technical understanding of the basics of Azure ML. The article demonstrates how to train and deploy a simple model, but you'll be able to apply the same concepts to your own ML projects, regardless of their complexity.ß

👓 View today's article​

Today's article.

🙋🏾‍♂️ Questions?​

You can ask questions about this post on GitHub Discussions

📍 30 days roadmap​

What's next? View the #30DaysOfAzureAI Roadmap

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