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.
Related jobs
Jobs that call for the skills explored in this talk.
Wilken GmbH
Ulm, Germany
Senior
Amazon Web Services (AWS)
Kubernetes
+1
CARIAD
Berlin, Germany
Junior
Intermediate
Python
C++
+1
Matching moments
02:29 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.
03:45 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.
03:38 MIN
Introducing the Azure Machine Learning platform and workspace
Introduction to Azure Machine Learning
02:43 MIN
Building enterprise-grade AI agentic solutions
AI for Enterprise Developers - Dr. Damir Dobric
03:27 MIN
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
Vikings language, the speech of the king Vasa or today's Swedish? Text classification with ML.NET.
01:33 MIN
The convergence of ML and DevOps in MLOps
AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment
03:27 MIN
Training, evaluating, and debugging the ML model
Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML
Featured Partners
Related Videos
54:27Introduction to Azure Machine Learning
Jose Luis Latorre Millas
47:19The state of MLOps - machine learning in production at enterprise scale
Bas Geerdink
27:23From ML to LLM: On-device AI in the Browser
Nico Martin
57:46Overview of Machine Learning in Python
Adrian Schmitt
41:45From Traction to Production: Maturing your LLMOps step by step
Maxim Salnikov
26:55How Machine Learning is turning the Automotive Industry upside down
Jan Zawadzki
2:11:02Intelligent Automation using Machine Learning
Boris Krumrey & Andreas Palfi & Radu Pruna
34:21DevOps for AI: running LLMs in production with Kubernetes and KubeFlow
Aarno Aukia
Related Articles
View all articles
.gif?w=240&auto=compress,format)
From learning to earning
Jobs that call for the skills explored in this talk.
WeCloudData
Remote
Python
Machine Learning
Continuous Integration
LinkiT
Amsterdam, Netherlands
Azure
DevOps
Python
PySpark
Terraform
+2
Neural Concept
Großmehring, Germany
Fluid
Python
Machine Learning
Zendesk
Berlin, Germany
Remote
API
Python
FastAPI
Machine Learning
+1
DKV Euro Service GmbH + Co. KG
Ratingen, Germany
Remote
Intermediate
Azure
Python
Docker
Terraform
+3