Columbus, Ohio, United States
Did you know you can train and use machine learning inside of your .NET applications without needing detailed knowledge of machine learning algorithms? In this talk we'll explore the ML.NET AutoML API capabilities and how accessible machine learning in C# really is as we write C# code to solve machine learning problems.
We'll focus heavily on the automatic algorithm selection features of AutoML in ML.NET and talk about the various tasks it can achieve before drilling deeper to apply AutoML to solve a multi-class classification problem. We'll train a machine learning model and have it predict video game ESRB ratings for a few hypothetical games provided by the audience, then host this new model in a .NET 6 minimal Web API project in ASP .NET Core.
We'll also explore evaluating model performance, ideal training times, and how trained models can be saved and loaded for use in production applications, as well as some places you can go to learn more about ML.NET and machine learning in general.
NOTE: This is different than the AutoML CLI or Model Builder. Our core focus is on C# code for Machine Learning using the AutoML as baby steps into Machine Learning
This talk is a mixture of slides and code.
This talk can be performed in person without an internet connection
After several decades as a software engineer and engineering manager, Matt now serves as a software engineering instructor at Tech Elevator where he gets to raise up future developers and unleash them upon the world to build awesome things.
Matt is an Azure Data Scientist Associate, a current organizer for the Central Ohio .NET Developer Group, runs a data science blog and YouTube channel, and is currently pursuing a master's degree in data analytics.
In his copious amounts of spare time, Matt continues to build nerdy things and looks for ways to share them with the community.