Data Scientist

Data Science with Azure: End-to-End Workflows

Building machine learning models in the cloud requires more than statistical knowledge—you need to understand how to configure workspaces, train at scale, and deploy models that actually work in production. This course teaches you to orchestrate the entire data science lifecycle in Azure: from setting up your environment, to training with Python scripts or automated ML, to deploying models as real-time endpoints that applications can call.

You'll use Jupyter notebooks, SDKs, and the CLI to build production-grade workflows, manage compute resources efficiently, and integrate trained models into enterprise applications. This is practical, hands-on training for data scientists who want to move beyond experimentation into deployment and monitoring.