WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
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.

Pause
Mute Enter Fullscreen
#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 · World Congress 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 · World Congress 2025

3:48 min

Question and answer on predictions, tooling, and datasets

Daniel Gaszewski · World Congress 2023

2:15 min

Open-source community and machine learning frameworks

Gian Marco Iodice Gian Marco Iodice · World Congress 2025

1:12 min

Training and fine-tuning models natively using MLX

MIlan Todorović MIlan Todorović · World Congress 2025

More from WeAreDevelopers LIVE

WeAreDevelopers LIVE - Is CSS Catching Up to Flash? thumbnail
1:03:33
8 days ago

WeAreDevelopers LIVE - Is CSS Catching Up to Flash?

Chris Heilmann, Daniel Cranney, Bramus Van Damme

WeAreDevelopers LIVE - Project Chopin: Make Your Team and Agents Play Along thumbnail
1:21:17
15 days ago

WeAreDevelopers LIVE - Project Chopin: Make Your Team and Agents Play Along

Chris Heilmann, Daniel Cranney, Krzysztof Cieślak

Fake or News - AI Toothbrushes, Carrier Pigeon Tech & Life on Mars - Krzysztof Cieślak thumbnail
4:23
17 days ago

Fake or News - AI Toothbrushes, Carrier Pigeon Tech & Life on Mars - Krzysztof Cieślak

Chris Heilmann, Daniel Cranney

WeAreDevelopers LIVE - Open Source Agent Toolkits thumbnail
1:00:38
22 days ago

WeAreDevelopers LIVE - Open Source Agent Toolkits

Chris Heilmann, Daniel Cranney, Eddie Jaoude

Fake or News - AI Music Detectors, Translating Ancient Languages & Tiny Heads in Teslas - Eddie Jaoude thumbnail
6:16
24 days ago

Fake or News - AI Music Detectors, Translating Ancient Languages & Tiny Heads in Teslas - Eddie Jaoude

Chris Heilmann, Daniel Cranney

Fake or News: AI Agents Hire Each Other, Spider-Man Ads in Cars & RAM Thieves - Marc Plogas thumbnail
5:46
29 days ago

Fake or News: AI Agents Hire Each Other, Spider-Man Ads in Cars & RAM Thieves - Marc Plogas

Chris Heilmann, Daniel Cranney, Marc Plogas

Upcoming sessions on this topic

Open session

World Congress 2026 North America

September 25, 2026 · 16:50–17:20

Stage 7

You Can’t Rerun Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

September 24, 2026 · 17:30–18:00

Stage 6

No Single Model to Rule Them All: Building Resilient AI Agents Across Open & Closed LLMs

Emmanuel Acheampong

Senior Manager Developer Relations at Crusoe AI

Emmanuel Acheampong
Open session

World Congress 2026 North America

September 25, 2026 · 13:30–14:00

Stage 7

It’s Alive! Taming the MLOps Franken-Stack: Write, Run, and Serve with Michelangelo

Eric Wang, Paul Zimmerman

Eric Wang
Paul Zimmerman
Open session

World Congress 2026 North America

September 23, 2026 · 10:00–17:00

Stage 11

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

September 25, 2026 · 14:50–15:20

Stage 4

Intelligence in Motion: Building the Next Generation of AI-Powered Apps on Zoom's Developer Platform

Brendan Ittelson

Chief Ecosystem Officer of Zoom

Brendan Ittelson
Open session

World Congress 2026 North America

September 24, 2026 · 14:10–14:40

Stage 5

Edge AI: Running Agentic Intelligence Where Internet Can't Reach

Nitin Eusebius

AWS - Principal Solutions Architect

Nitin Eusebius

Related videos

Introduction to Azure Machine Learning thumbnail
54:27
LIVE • over 4 years ago

Introduction to Azure Machine Learning

Jose Luis Latorre Millas

MLOps - What’s the deal behind it? thumbnail
29:24
World Congress 2022 • over 4 years ago

MLOps - What’s the deal behind it?

Nico Axtmann

Making neural networks portable with ONNX thumbnail
54:49
JS Congress • almost 5 years ago

Making neural networks portable with ONNX

Ron Dagdag

Deployed ML models need your feedback too thumbnail
37:24
World Congress 2021 • about 5 years ago

Deployed ML models need your feedback too

David Mosen

Getting Started with Machine Learning thumbnail
44:11
LIVE • almost 5 years ago

Getting Started with Machine Learning

Alexandra Waldherr

The state of MLOps - machine learning in production at enterprise scale thumbnail
47:19
LIVE • over 4 years ago

The state of MLOps - machine learning in production at enterprise scale

Bas Geerdink

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.