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WeAreDevelopers LIVE • Oct 27, 2021

Machine Learning in ML.NET

Marco Zamana

Stop treating machine learning as a siloed system. ML.NET lets developers package AI models as familiar NuGet dependencies, enabling continuous training loops alongside standard .NET code.

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#1 about 3 min

Overview of ML.NET for .NET developers

How .NET developers can build intelligent applications natively using the open-source, cross-platform ML.NET framework.

#2 about 2 min

Different methods for utilizing ML.NET tools

How developers can consume ML.NET via the API, command-line interface, or Visual Studio Model Builder.

#3 about 3 min

Core components and model extension capabilities

Core components like IDataView, transformers, and extension libraries enable seamless model training and ONNX or TensorFlow consumption.

#4 about 3 min

Understanding the machine learning building workflow

Standardizing the machine learning workflow requires continuous data preparation, model training loops, and scalable prediction engines.

#5 about 9 min

Using ML.NET CLI and MLOps principles

Integrating the ML.NET CLI for rapid training into continuous delivery pipelines ensures models remain accurate through automated retraining.

#6 about 12 min

Binary classification with Visual Studio Model Builder

Accelerating binary classification tasks by training a sentiment analysis model directly within Visual Studio using Model Builder.

#7 about 3 min

Evaluating and integrating the generated model artifacts

Evaluating the trained model's accuracy and scaffolding a web API solution facilitates rapid downstream consumer integration.

#8 about 7 min

Training object detection with Azure Custom Vision

Leveraging Azure Custom Vision to upload images, tag subjects, and iterate on object detection algorithms.

#9 about 5 min

Exporting and evaluating learned ONNX models

Exporting a trained model in ONNX format and inspecting its required tensor inputs using Netron prepares it for backend integration.

#10 about 21 min

Integrating an ONNX model in a console application

Solving custom offline object detection by configuring an inference pipeline and executing the ONNX model within a C# console application.

#11 about 9 min

Audience Q&A on datasets and algorithm selection

Guidance on navigating optimal dataset sizes, learning foundational machine learning concepts, and selecting appropriate algorithms.

Matching moments

2:29 min

Building machine learning workflows with the ML.NET framework

Daniel Gaszewski · WWC 2023

1:20 min

Navigating the components of Azure Machine Learning platform

Jose Luis Latorre Millas · LIVE

4:13 min

Implementing machine learning with Core ML and Vision

MIlan Todorović MIlan Todorović · WWC 2025

3:48 min

Question and answer on predictions, tooling, and datasets

Daniel Gaszewski · WWC 2023

2:15 min

Open-source community and machine learning frameworks

Gian Marco Iodice Gian Marco Iodice · WWC 2025

1:12 min

Training and fine-tuning models natively using MLX

MIlan Todorović MIlan Todorović · WWC 2025

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