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.
#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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Matching moments
02:29 MIN
Getting started with the ML.NET framework
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03:45 MIN
Q&A on ML.NET, data, and model capabilities
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03:38 MIN
Introducing the Azure Machine Learning platform and workspace
Introduction to Azure Machine Learning
01:54 MIN
Real-world applications and key takeaways
Machine learning 101: Where to begin?
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Understanding the new AI developer stack and MLOps workflow
Developer Experience, Platform Engineering and AI powered Apps
02:53 MIN
How to train a model using the ML.NET UI
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The convergence of ML and DevOps in MLOps
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03:27 MIN
Training, evaluating, and debugging the ML model
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