In this workshop, you will learn how to infuse Machine Learning capabilities into your .NET applications with ML.NET. You'll learn the basic building blocks of ML.NET and how you can use the library to build machine learning applications in both online and offline scenarios.
We will cover how you can use ML.NET to extract data from both relational and non-relational data sources and prepare it for machine learning tasks, what type of machine learning algorithms we can apply onto our data depending on what questions we want to ask from our data, how we can evaluate our model to determine how effective it is and how we can use our model in a variety of different applications.
- .NET developers who are interested in Machine Learning and want to leverage their existing .NET skills to build ML applications.
- ML Engineers who want to learn about how they can productionize their machine learning models using ML.NET.
- Data Engineers and Data Scientists who are curious about how they can use ML.NET to build Machine Learning applications
If you want to see how you can use your existing .NET skillset to build applications with Machine Learning capabilities, then this workshop is for you.
1. What is ML.NET? API vs Model Builder and basic Architecture
2. Discovering what type of machine learning tasks we can perform using ML.NET.
3. Building a basic model using ML.NET using data files.
4. Building an ML.NET application that uses relational data from a SQL database.
5. Building an end-to-end application that uses non-relational data in Azure Functions.
I'm a Software Engineer at ASB Bank in New Zealand. I spend most of my time using .NET and Azure, but I also enjoy hacking on projects using a variety of technologies.
Outside from work, I love to kayak and hike when I get the chance.