Marco Zamana
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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Matching moments
12:44 MIN
Getting started with the ML.NET framework
Vikings language, the speech of the king Vasa or today's Swedish? Text classification with ML.NET.
24:06 MIN
Q&A on ML.NET, data, and model capabilities
Vikings language, the speech of the king Vasa or today's Swedish? Text classification with ML.NET.
18:36 MIN
How to train a model using the ML.NET UI
Vikings language, the speech of the king Vasa or today's Swedish? Text classification with ML.NET.
26:42 MIN
Training, evaluating, and debugging the ML model
Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML
22:41 MIN
Introducing the Azure AI platform for end-to-end LLMOps
From Traction to Production: Maturing your LLMOps step by step
02:38 MIN
Common challenges in developing machine learning applications
Data Fabric in Action - How to enhance a Stock Trading App with ML and Data Virtualization
09:51 MIN
Understanding the machine learning development lifecycle
Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML
02:20 MIN
Exploring Apple's core machine learning frameworks
Harnessing Apple Intelligence: Live Coding with Swift for iOS
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