Azure AI for Machine Learning Developers
Welcome to the third week of AI April #30DaysOfAzureAI. Last week we covered Azure AI for Intelligent App Developers.
This week is for people building machine learning models or deploying models to production. You’ll learn about Azure ML model development, components, pipelines, and endpoints, as well as debugging Responsible AI and MLOps.
If you are catching up, then #30DaysOfAzureAI is a series of daily posts throughout April. Hear from our experts in the product teams, cloud advocacy, community and follow along at your own pace!
Be sure to follow along on Twitter or LinkedIn on hashtag #30DaysOfAzureAI.
This week we are covering the following topics
April 17 — 🧑🏽🔬 Scaling Model Dev with Azure ML
Dive into Azure ML and learning how to train and deploy ML models at scale. If you’re a Data Scientist, ML, or MLOps engineer, this one’s for you!
April 18 — 🧑🏽🔬 Streamline Model Dev with Azure ML
Unlock the potential of your ML projects with Azure ML Components & Pipelines. Streamline your development and boost productivity.
April 19 — 🧑🏽🔬 Azure ML Managed Online Endpoints
Learn about Azure Machine Learning’s managed online endpoints! With built-in security, blue/green deployments, and auto-scaling, it’s the perfect way to deploy and scale your models.
April 20 — 🧑🏽🔬 Debug models with Responsible AI
Unlock the power of Responsible AI with the RAI Dashboard, a suite of open-source tools for model debugging, fairness assessment, and more, now available on Azure Machine Learning.
April 21 — 🧑🏽🔬 Streamline Ops with Azure MLOps
Unleash your #MachineLearning potential! Join Cassie and Korey in today’s #LearnLive and discover #Azure MLOps. Streamline your ML lifecycle with open source tooling, and take your models to the next level!
Questions or introduce yourself
If you have questions related to Azure AI or you’d like to introduce yourself then head to Azure AI Devs GitHub Discussions.
Enjoy.
