Skip to main content

20. 🧑🏽‍🔬Streamline Ops with Azure MLOps

· 3 min read
Cassie Breviu

Please share


🗓️ Day 20 of #30DaysOfAzureAI

Learn about Azure Machine Learning lifecycle and open source tooling to start building MLOps

Yesterday we learned about the Azure ML Responsible AI Dashboard. Today is an introduction to Azure MLOps, where you'll learn how to "productionalize" ML models with Azure ML.

🎯 What we'll cover

  • Azure ML MLOps
  • Practices and tools for managing the machine learning lifecycle.
  • Improving data scientists and developer collaboration.

Image banner for day 20

📚 References

🚌 Learn Live MLOps to streamline the ML Lifecycle

Watch today's video about MLOps for the ML Lifecycle. The presenters, Cassie and Korey, start with an overview of MLOps, explaining that it is a set of practices and tools that help data scientists and developers work together to build and deploy machine learning models.

Cassie and Korey discuss the benefits of MLOps, including faster time to value, improved collaboration, and increased model quality. They also discuss the challenges of MLOps, including the need for a common language, the need for a common platform, and the need for a common process.

👓 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

🧲 Subscribe