Azure Machine Learning Step 3: Compute & Environments

In this third session of the Azure Machine Learning series, we’ll focus on the infrastructure that powers your machine learning workloads: compute and environments. After preparing and organizing your data, the next step is understanding how Azure ML runs your code, scales your workloads, and ensures consistent execution across development and production.

This session explores the practical side of working with compute targets and environments in Azure ML. You’ll learn how to select the right compute for your workloads, configure environments for reproducibility, and optimize performance and cost. Whether you’re continuing from Step 2 or already experimenting with training jobs, this session will help you build a solid foundation for running machine learning workloads efficiently in Azure.

You’ll learn:

• How compute and environments fit into the machine learning lifecycle
• The different compute options in Azure ML (compute instances, compute clusters, serverless, and attached compute)
• How to create and manage compute resources using the Studio UI, SDK, and CLI
• How to define and use environments (conda, Docker) for consistent and reproducible runs
• Techniques for selecting the right VM sizes (CPU vs GPU) based on workload needs
• How to manage dependencies and avoid common environment issues
• Best practices for scaling, cost optimization, and auto-scaling compute clusters
• How compute and environments are used in training jobs, pipelines, and deployments

This session is designed to help you move from “I have data ready” to “I can reliably run and scale my ML workloads.” If you’re ready to understand what’s happening behind the scenes and ensure your models run consistently across environments, this is your next step.