Marco Zamana

Machine Learning in ML.NET

What if you could add machine learning to your .NET apps without being a data scientist? Discover how ML.NET's Model Builder automates the process.

Machine Learning in ML.NET
#1about 6 minutes

Introducing ML.NET for .NET developers

ML.NET is a cross-platform, open-source framework that allows developers to integrate custom machine learning models into any .NET application.

#2about 11 minutes

Understanding the machine learning workflow and MLOps

The machine learning process involves a continuous cycle of preparing data, building and training models, deploying them, and monitoring for retraining.

#3about 8 minutes

Building a model with the Visual Studio Model Builder

A step-by-step guide shows how to use the Model Builder UI in Visual Studio to train a sentiment analysis model without writing code.

#4about 7 minutes

Exploring the auto-generated C# code from Model Builder

An analysis of the scaffolded code reveals key ML.NET concepts like ModelInput, ModelOutput, the data processing pipeline, and the MLContext class.

#5about 6 minutes

Training an object detection model with Azure Custom Vision

Learn how to use the Azure Custom Vision service to upload, tag, and train an image-based object detection model for export.

#6about 25 minutes

Consuming an ONNX model in a .NET console application

Write C# code from scratch to load a pre-trained ONNX model, build a data transformation pipeline, and make predictions for object detection.

#7about 8 minutes

Q&A on data, learning resources, and algorithms

The session concludes with answers to common questions about dataset size, getting started with ML.NET, platform support, and algorithm selection.

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