Room 2 

16:20 - 17:20 


Talk (60 min)

Beyond Sentiment Analysis: Object Detection with ML.NET

According to various researches, AI can improve manufacturing defect detection rates by up to 90% with the help of computer vision. Not only this, it also helped save $2 billion of counterfeit bills, just in the US.

Machine Learning

This clearly depicts that Computer Vision is very essential and productive for many industry verticals. Despite its importance for businesses, it requires a handful of resources to detect or classify an object from the given media or image.

But would you like to learn how can you overcome this problem and implement object detection in your solutions?

Easy peasy! ML.NET solves this problem for you and we're going to see how you can now detect images using the latest ML.NET Model Builder without having to worry about your slower machine and I will brief you why's that!

In addition to this, we'll also learn about the tools it take to label your images as well as some other cool examples and some use-cases of ML.NET. All in all, it's going to be fun for folks who belong to the great .NET world!

Arafat Tehsin

Arafat Tehsin is a Solution Architect at EY and Microsoft MVP (AI) based in Sydney, Australia. He has a passion for solving complex business problems using innovative technologies and delivering high-quality solutions. He has more than a decade of software development experience with a specialization on digital transformation and business automation. He has worked with Global SIs of Microsoft and developed some next-gen solutions using Azure AI and .NET with the empowerment of our business users by Power Platform and more.

Arafat co-founded Global AI - The Podcast, which talks about everything around Microsoft AI with guests from different backgrounds and domains. He is also a speaker at various developer conferences, such as NDC Sydney, Global AI Bootcamp and THAT Conference, where he shares his insights on building next-gen solutions using cutting-edge Microsoft technologies. Arafat is always keen to learn new things and explore new possibilities in the field of Applied AI.