This session introduces Machine Learning from first principles, explaining the core concepts in a clear and accessible way. It clarifies how Machine Learning relates to Artificial Intelligence, showing how it is a branch of AI with a distinct purpose and approach, helping attendees understand where each fits.

The focus then moves to AutoML, exploring what is actually being automated across the machine learning lifecycle and why this is valuable for both newcomers and experienced practitioners. Realistic examples are used to demonstrate the types of problems that can be solved using Azure AutoML.

The session is built around three demonstrations. The first provides a guided tour of Azure Machine Learning Studio. The second shows how to create an AutoML model end to end, from data ingestion through training and deployment as a web service, with a brief coded example in VS Code using the Azure ML extension to show a faster, code-first approach. The final demo demonstrates how to consume predictions from the deployed model using a simple Excel and VBA interface, making machine learning outputs accessible through a familiar tool.

This session is aimed at data professionals who want a practical understanding of what machine learning and AutoML are, how they work, and how they can be applied using the Azure platform.

By attending this session, you will understand the difference between AI and machine learning, learn what AutoML automates across the ML lifecycle, see how to build and deploy AutoML models in Azure, and learn how to consume model predictions in tools such as Excel.