Inside Azure Machine Learning Workspace
Explore the core components of the Azure Machine Learning Workspace and see how each tool supports the ML Ops lifecycle. From building pipelines to managing models, endpoints, compute resources, and experiments, this walkthrough shows how Azure ML helps organize, automate, and scale machine learning projects.
Discover how these tools work together to improve reproducibility, streamline deployment, and support continuous improvement—so machine learning solutions remain reliable, efficient, and production-ready.
00:00 Introduction to Azure ML Workspace
00:33 Accessing and Launching Azure ML St…
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Chapters (8)
Introduction to Azure ML Workspace
0:33
Accessing and Launching Azure ML Studio
1:01
Designer & Pipelines Overview
1:48
Models & Versioning for Reproducibility
2:17
Endpoints & Model Deployment
2:46
Compute Instances vs. Compute Clusters
3:28
Jobs & Experiment Tracking
4:09
Understanding the ML Ops Lifecycle
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