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🗓️ 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.
📚 References
- Machine learning operations (MLOps)
- Learn Module: Introduction to machine learning operations (MLOps)
- Learn Module: Start the machine learning lifecycle with MLOps
🚌 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?
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📍 30 days roadmap
What's next? View the #30DaysOfAzureAI Roadmap
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