Overview of Azure AI

Introduction Introduction

Introduction

Artificial intelligence (AI) is on everyone's mind at the moment, both its role in the wider world and its applications in research. In many ways, it has become a buzzword for companies and research bodies looking to stay ahead without truly understanding how and when it should be applied. Nevertheless, when it and its outputs are understood, AI has many real-world applications in a researcher's day-to-day work, as well as specific uses within various academic domains.

However, for many researchers, especially those without a coding or technical background, the journey from recognising the potential of AI to actually implementing it in their workflow can be overwhelming.

"Should I be learning Python?"
"Do I need to understand neural networks?"
"What's the difference between machine learning and AI?"
"Do I need specialised hardware?"

The above questions can create a paralysis that keeps valuable AI tools out of reach for precisely the scientists who could benefit most from them. This course should help ramp up your understanding of how to prctically implement AI within Azure without having to learn a whole new domain set of skills.

What Azure offers

This is where Microsoft Azure and this resource could help, not as yet another complicated system to master, but as a bridge connecting your existing research questions to powerful AI capabilities without requiring intensive learning of Python and AI algorithms to facilitate. Azure AI Studio can hopefully grow with you, scaling from beginner-friendly interfaces to more advanced tools as your knowledge of AI systems develops.

We hope that after completing this course, we will have demystified many aspects of AI, given you the tools to use Azure independently, and have developed your own trained AI tools that can be immediately applied to your research.

But first, let's gain an understanding of what Azure is, what cloud computing is, and what that practically means for you as researchers.