mlnet

Name: mlnet
Author: managedcode

461stars35forks

How to install mlnet?

managedcode/dotnet-skills/mlnet

$npx -y skills add managedcode/dotnet-skills --skill mlnet

Installs into the current project.

Summary

Use ML.NET to train, evaluate, or integrate machine-learning models into .NET applications with realistic data preparation, inference, and deployment expectations. USE FOR: ML.NET integration; local model training or retraining; inference pipelines, model loading, evaluation, and

Files · 1

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SKILL.md

1	# ML.NET
2
3	## Trigger On
4
5	- integrating machine learning into a .NET application
6	- training or retraining ML.NET models from local data
7	- reviewing inference pipelines, model loading, or AutoML-generated code
8
9	## Workflow
10
11	1. Start from the prediction task and data quality, not the algorithm or package list.
12	2. Separate training code from inference code so the production path stays lean and predictable.
13	3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
14	4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.
15	5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
16	6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.
17
18	## Deliver
19
20	- ML.NET pipelines that fit the prediction task
21	- production-usable inference integration
22	- evaluation evidence tied to the business scenario
23
24	## Validate
25
26	- model quality is measured, not assumed
27	- training and inference responsibilities are separated
28	- deployment and versioning expectations are explicit
29
30	## References
31
32	- [patterns.md](references/patterns.md) - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns
33	- [examples.md](references/examples.md) - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML

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mlnet

461stars35forks

View on GitHub

How to install mlnet?

managedcode/dotnet-skills/mlnet

$npx -y skills add managedcode/dotnet-skills --skill mlnet

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# ML.NET
2
3	## Trigger On
4
5	- integrating machine learning into a .NET application
6	- training or retraining ML.NET models from local data
7	- reviewing inference pipelines, model loading, or AutoML-generated code
8
9	## Workflow
10
11	1. Start from the prediction task and data quality, not the algorithm or package list.
12	2. Separate training code from inference code so the production path stays lean and predictable.
13	3. Review feature engineering, normalization, label quality, and evaluation metrics before trusting model output.
14	4. Use Model Builder or the ML.NET CLI when they speed up exploration, but inspect the generated C# before treating it as production architecture.
15	5. Plan how the model is loaded, versioned, and refreshed in the application lifecycle.
16	6. Validate with representative datasets and explicit evaluation, not only with a sample that happens to run.
17
18	## Deliver
19
20	- ML.NET pipelines that fit the prediction task
21	- production-usable inference integration
22	- evaluation evidence tied to the business scenario
23
24	## Validate
25
26	- model quality is measured, not assumed
27	- training and inference responsibilities are separated
28	- deployment and versioning expectations are explicit
29
30	## References
31
32	- [patterns.md](references/patterns.md) - Data loading, training pipelines, evaluation metrics, deployment strategies, and feature engineering patterns
33	- [examples.md](references/examples.md) - Complete examples for sentiment analysis, price prediction, image classification, anomaly detection, recommendations, clustering, fraud detection, text classification, object detection, and AutoML

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Type