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Today I'm going to be talking to you about Machine Learning and Artificial Intelligence for every developer with ML.NET and Visual Studio. Dot NET has been a great platform for building any kind of application ranging from desktop, web, Cloud, mobile, gaming, IoT. ML.NET is a machine learning framework for for.NET developers. You can build your own models. It's proven and extensible, it's been used heavily inside of Microsoft across all groups.
Its open-source and cross-platform, so it works on Windows Mac and Linux and it's highly developer focused in terms of the scenarios that we're enabling and how you can build with ML.NET. ML.NET is available at (.NET/ML). Here are some of the machine learning scenarios in ML.NET.
You can do:-
1) Sentiment analysis2) Issue classification3) Ranking4) Image classification5) Forecasting6) Predictive maintenance7) Recommendation8) Customer Segmentation
The idea behind these is all of the developers know scenarios versus knowing ML tasks, so ML.NET is very approachable for.NET developers. ML.NET is proven at scale. It's enterprise-ready. Microsoft has been using internally ML.NET for the last 10 years across various products. If you've seen Bing ads or you've done Chart Recommendations, Design Ideas in PowerPoint, Windows Defender, Anomaly Detection in Azure Stream Analytics, Key Influencers in Power BI and a lot more product groups using ML.NET at a scale that over years, Microsoft made the Machine Learning tech behind it perform much better by dogfooding this ourselves. Beyond internal customers, Microsoft has lots of external customers using ML.NET at scale in production as well.
SigParser is an example of a customer using ML.NET. SigParser is trying to make the CRM management much easier by automating various tasks around e-mail parsing, e-mail classification, entity extraction, and here's an example use case of SigParser using ML.NET. With ML.NET, they were able to train the model and immediately test inside of the code. This makes shipping new changes faster because all of the toolings were in one place. The great benefit of ML.NET is a developer focus framework, so it integrates with your existing toolsets across CLI, across Visual Studio, across DevOps, across CI/CD. So it is just another .NET library that you would use in your application. SigParser was able to take these advantages of ML.NET and improve their productivity for their own business.
So I hope you enjoyed this short article about ML.NET in Visual Studio 2019. ML.NET is a framework for.NET developers to use Machine Learning. It's open-source and it's proven on the scale since it's being used internally at Microsoft. If you're interested in trying out ML.NET, here are some links to getting started. Microsoft has lots of samples that you can follow and for your use case, you can read the Getting Started tutorials. If you have a feature or a request for a new area to add to ML.NET, you can follow this link, and if you're looking to use ML.NET in production, you can use this link to reach out to an ML.NET engineer who can help you get to production as well.
Getting Started at ( http://dot.net/ml )
Try the samples at ( http://aka.ms/mlnetsamples )
Read the Docs at ( http://aka.ms/mlnetdocs )
Request features or contributes at ( http://aka.ms/mlnet )
Get help for going into production ( http://aka.ms/mlnetprod )
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