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Binary Classification on Azure ML: Is this Red Wine Good or Bad?

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Presentation from Global AI Night Reston (Sept 5, 2019)

In it, I cover the visual tooling in Azure to do a binary classification on whether or not a red wine is considered good.

Publié dans : Technologie
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Binary Classification on Azure ML: Is this Red Wine Good or Bad?

  1. 1. Global AI Bootcamp Is that red wine good or bad? How to use Azure Machine Learning Visual Interface to build ML models with no code to predict red wine quality. Frank La Vigne FrankLa@Microsoft.com www.FranksWorld.com | www.DataDriven.TV | www.DataSoupSummit.com
  2. 2. Frank La Vigne AI Cloud Solution Architect tableteer Fun Fact: La Vigne means vineyard in French
  3. 3. Virtual Summit 6 Speakers from around the world 30% Discount Use code NOVASQL
  4. 4. Upcoming Events • Thursday, Sept 26 – Chevy Chase - Azure Cosmos DB Workshop (Free) - https://azurecosmosdbworkshop-sep262019.eventbrite.com • Friday, Oct 11 – Reston – Fall 2019 Azure Data Fest ($20.00) - https://fall2019restonazuredatafest.eventbrite.com • Thursday, Oct 24 – Chevy Chase – Azure Databricks Workshop - https://azuredatabricksworkshop-oct242019.eventbrite.com • Friday, Oct 25 – Online - Data Soup Summit: Data Ops ($17.00) • Saturday, Dec 14 – Reston - 2019 Reston Global AI Bootcamp - https://restonglobalaibootcamp2019.eventbrite.com
  5. 5. AI IS EASIER THAN AV
  6. 6. every every Artificial Intelligence Training Democratized
  7. 7. Business Problem: “I need a bookshelf.”
  8. 8. How to Bookshelf? •Buy •Assemble •Build
  9. 9. We have AI Tools to Meet You Where You Are PowerApps Cognitive Services Rich AI Tools
  10. 10. Workshop Part 1 Azure Machine Learning Visual Interface Is that red wine good or bad? Using ML Studio
  11. 11. Workshop Part 2 Azure Machine Learning Visual Interface Is that red wine good or bad? Using Raw Python
  12. 12. Machine Learning Algorithm Computation Computation
  13. 13. TRADITIONAL DEVELOPMENT PARADIGM Rules Data Answers
  14. 14. MACHINE LEARNING PARADIGM Answers Data Rules
  15. 15. LET’S TALK CAKE
  16. 16. LET’S EXPLORE THIS ANALOGY Unsupervised Learning (Cake) •Large amount of samples Supervised Learning (Icing) •Less samples Reinforcement Learning (Cherry on top) •Even less samples Transfer Learning (Candle) •Least amount of new samples over time
  17. 17. FOR EXAMPLE Given a picture set of cats and dogs • Supervised Learning • You tell the computer which photos contain a cat and which ones that contain a dog • Unsupervised Learning • You give the computer pictures of cats and pictures of dogs • Reinforcement Learning • You reward the computer for right answers
  18. 18. TRANSFER LEARNING • Which one is the hunter? • Which one is the hunted?
  19. 19. ELEVATOR PITCH • Supervised  you know the answers already • Rules are inferred • Unsupervised  you don’t know the answers • A pattern emerges • Reinforcement  you figure out the answer • Through trial and error • Transfer  you rely on previous answers • A model trained on one task is re-purposed
  20. 20. PUT ANOTHER WAY
  21. 21. Supervised Multiclassification Example Age Income Education Gender Housing 61 $65,000 Moderate F Own 42 $72,000 High F Rent 18 $25,000 Moderate M Other 22 $36,000 Low M Rent 31 $52,000 High M ?
  22. 22. Operationalize Model The Model Building Process Prepare Data Raw Data Prepared Data Apply preprocessing to data Deploy Chosen Prod Model Application posts to API Train Model Apply learning algorithm to data Select Candidate model Test Model Test Candidate Model with unseen data Select good enough model
  23. 23. What engine(s) do you want to use? Tools & Services Which experience do you want? Build your own or consume pre- trained models? Microsoft AI Platform Build your own model Azure Machine Learning Code-first Machine Learning Services SQL Server Spark / DataBricks Hadoop Azure Batch DSVM Azure Container Service Visual-tooling Machine Learning Studio Use pre-built models Cognitive Services, Bot Services Customize? Machine Learning/AI tools When to use what?

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