Marco Zamana

Machine Learning in ML.NET

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.

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.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

Getting started with the ML.NET framework
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.

Q&A on ML.NET, data, and model capabilities
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.

Introducing the Azure Machine Learning platform and workspace
08:10 MIN

Introducing the Azure Machine Learning platform and workspace

Introduction to Azure Machine Learning

Real-world applications and key takeaways
24:31 MIN

Real-world applications and key takeaways

Machine learning 101: Where to begin?

Understanding the new AI developer stack and MLOps workflow
12:16 MIN

Understanding the new AI developer stack and MLOps workflow

Developer Experience, Platform Engineering and AI powered Apps

How to train a model using the ML.NET UI
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.

The convergence of ML and DevOps in MLOps
01:58 MIN

The convergence of ML and DevOps in MLOps

AI Model Management Life Circles: ML Ops For Generative AI Models From Research to Deployment

Training, evaluating, and debugging the ML model
26:42 MIN

Training, evaluating, and debugging the ML model

Leverage Cloud Computing Benefits with Serverless Multi-Cloud ML

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Jobs that call for the skills explored in this talk.