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Oleksander Krakovetskyi "Artificial Intelligence and Machine Learning for .NET developers"


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Microsoft offers a lot of tools and services for creating intelligent apps - ML.NET, Azure Cognitive Services, Azure Machine Learning Service, Azure Machine Learning Studio, Bot Framework and other.

How to select the appropriate tool? What problems I can solve and how to do that? Let's talk about this.

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Oleksander Krakovetskyi "Artificial Intelligence and Machine Learning for .NET developers"

  1. 1. Artificial Intelligence and Machine Learning for .NET developers Oleksandr Krakovetskyi DevRain
  2. 2. About 1. CEO DevRain 2. CTO DonorUA 3. Ph.D. in Computer Science 4. Microsoft Regional Director 5. Microsoft Artificial Intelligence Most Valuable Professional
  3. 3. AI Tools Visual Studio Tools for Artificial Intelligence Azure Machine Learning for Visual Studio Code Azure Machine Learning Studio
  4. 4. Pre-built AI Azure Cognitive Services RESTful intelligent APIs for Vision, Speech, Language, Knowledge and Search Azure Cognitive Services Labs Early look at emerging Cognitive Services technologies
  5. 5. Custom AI Azure Machine Learning Service Accelerate the end-to-end machine learning lifecycle Azure Cognitive Services Custom Vision Microsoft Cognitive Services Custom Vision
  6. 6. Conversational AI LUIS Fast and effective way of adding language understanding to applications QnA Maker Service to train AI to respond to user's questions in a conversational way,
  7. 7. Conversational AI Dispatch Use multiple LUIS and QnA models dispatch?view=azure-bot-service-4.0&tabs=csharp Microsoft Bot Framework A framework for building enterprise-grade conversational AI experiences,
  8. 8. Demo
  9. 9. Machine Learning for .NET ML.NET An open source and cross-platform machine learning framework Sentiment analysis, Product recommendation, Price prediction, Customer segmentation, GitHub labeler, Fraud detection, Spam detection, Image classification, Sales forecasting.
  10. 10. Machine Learning
  11. 11. Machine Learning
  12. 12. Movie Recommender In the *.csv files, there are four columns: • userId (feature) • movieId (feature) • Rating (label) • timestamp
  13. 13. Movie Recommender Incredibles 2 (2018) The Avengers (2012) Guardians of the Galaxy (2014) User 1 Watched and liked movie Watched and liked movie Watched and liked movie User 2 Watched and liked movie Watched and liked movie Has not watched – RECOMMEND
  14. 14. Movie Recommender 1. The root of mean squared error (RMS or RMSE) is used to measure the differences between the model predicted values and the test dataset observed values. Technically it's the square root of the average of the squares of the errors. The lower it is, the better the model is. 2. R Squared indicates how well data fits a model. Ranges from 0 to 1. A value of 0 means that the data is random or otherwise can't be fit to the model. A value of 1 means that the model exactly matches the data. You want your R Squared score to be as close to 1 as possible.
  15. 15. Demo
  16. 16. Not Hotdog
  17. 17. Data Science VMs Comprehensive pre-configured virtual machines for data science modelling, development and deployment. us/services/virtual- machines/data-science-virtual- machines/
  18. 18. Microsoft Research Open Data A collection of free datasets from Microsoft Research to advance state-of-the-art research in areas such as natural language processing, computer vision, and domain specific sciences. Download or copy directly to a cloud-based Data Science Virtual Machine for a seamless development experience.
  19. 19. AI School Dive in and learn how to start building intelligence into your solutions with the Microsoft AI platform, including pre-trained AI services like Cognitive Services and Bot Framework, as well as deep learning tools like Azure Machine Learning, Visual Studio Code Tools for AI, and Cognitive Toolkit. Our platform enables any developer to code in any language and infuse AI into your apps. Whether your solutions are existing or new, this is the intelligence platform to build on.
  20. 20. Q&A Oleksandr Krakovetskyi @sashaeve