This course provides hands-on experience with Microsoft Azure's AI and ML services. You will learn to set up, manage, and troubleshoot Azure-based AI & ML workflows. The course covers the entire ML lifecycle in Azure, from data preparation to model deployment and monitoring. By the end of this course, you will be able to: 1. Configure and manage Azure resources for AI & ML projects. 2. Implement end-to-end ML pipelines using Azure services. 3. Deploy and monitor ML models in Azure production environments. 4. Troubleshoot common issues in Azure AI & ML workflows. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.
Walkthrough: Creating an ingestion pipeline (Optional)
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Skills You'll Learn
Cloud Resource Optimization, Model Deployment on Microsoft Azure, Microsoft Azure-based Troubleshooting, Microsoft Azure Machine Learning Service, End-to-end ML Workflow Management
From the lesson
Data preparation and model training in Azure
Taught By
Microsoft