In this workshop, we'll start from a CSV exported by a service, and go all the way to an application that uses Machine Learning to make clever decisions.

1. What does a developer need to know about Machine Learning?

2. How does ML.NET help getting started with ML?

3. Quickly prototype a solution with ML.NET Model Builder

4. Improve solution with simple data science rules

5. Integrate a machine learning solution into your application

1. Prototype a solution with multiple different ML.NET Model Builder models

3. Automating generation of models with ML.NET CLI

4. Continuously improving machine learning model and updating applications

2 Days

Getting Started with Machine Learning using ML.NET

Machine Learning

.NET

This hands-on workshop provides practical guidance and recommendations. We will cover architecture, technologies, tools, and frameworks. We will examine strategies for organizing your projects, folders, and files. We will design a system that is simple to build and maintain - now and well into the future.

You will leave this workshop having developed the foundation and structure for your next enterprise application, along with the knowledge to take your enterprise application development to the next level.

Required workload - ASP.NET and web development

You can also use your preferred IDE, e.g. Visual Studio Code or JetBrains Ryder.

NDC { Sydney }

Workshops

09:00 - 17:00 (UTC+10)

Practical Clean Architecture with .NET 6

Jason Taylor

Getting Started with Machine Learning using ML.NET

Jernej Kavka