Binary Classification on Azure ML: Is this Red Wine Good or Bad?
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The document advertises upcoming events related to AI and machine learning using Microsoft Azure services. It promotes a December 14, 2019 global AI bootcamp in Reston and provides links to register for upcoming workshops on Azure Cosmos DB, Azure Databricks, and Data Soup Summit. It also shares the speaker's contact information and links to their websites and social media profiles.
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Binary Classification on Azure ML: Is this Red Wine Good or Bad?
- 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. Frank La Vigne AI Cloud Solution Architect tableteer Fun Fact: La Vigne means vineyard in French
- 3. Virtual Summit 6 Speakers from around the world 30% Discount Use code NOVASQL
- 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. AI IS EASIER THAN AV
- 7. Business Problem: “I need a bookshelf.”
- 9. We have AI Tools to Meet You Where You Are PowerApps Cognitive Services Rich AI Tools
- 10. Workshop Part 1 Azure Machine Learning Visual Interface Is that red wine good or bad? Using ML Studio
- 11. Workshop Part 2 Azure Machine Learning Visual Interface Is that red wine good or bad? Using Raw Python
- 16. LET’S TALK CAKE
- 17. 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
- 18. 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
- 19. TRANSFER LEARNING • Which one is the hunter? • Which one is the hunted?
- 20. 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
- 21. PUT ANOTHER WAY
- 23. 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 ?
- 24. 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
- 25. 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?
Editor's Notes
- 0 min - (pre-workshop crowd engagement)
- 30s – introduce yourself and warm up the crowd Talk track -introduce yourself -talk about how this is a beginner workshop and no previous programming or machine learning knowledge is required.
- Microsoft Azure: the cloud for intelligent solutions In addition to having the traditional on-premises enterprise data tools—such as SQL Server—Azure provides SQL services that connects data to AI services. This enables quick adoption of technologies, such as deploying a global mobile application that integrates with facial recognition services. The ease of integration enables anyone to build solutions like that. From bot frameworks to cognitive services, you can fundamentally change the way your business goes to market with Microsoft’s power AI platform
- Workshop instructions can be found on github: https://github.com/cassieview/wine-quality-azure-ml-visual-interface
- Workshop instructions can be found on github: https://github.com/cassieview/wine-quality-azure-ml-visual-interface
- 5 min – explain machine learning Machine learning is a subfield of Artificial Intelligence. Technically, machine learning is a method of data analysis that automates analytical model building. But we can think of it as a technique to train artificially intelligent systems without needing to be specifically programmed. Here is a diagram I like that I think puts things into perspective a bit. So the overarching parent is AI – that covers machine learning and deep learning to simulate human intelligence. Machine learning is statistical methods that include deep learning and deep learning is a subset of machine learning that uses neural networks. Neural Networks are used for language, image classification problems and other deep learning problems. One funny, and true way of remembering the difference, is that when you’re trying to sell a product, you call it AI. When you’re trying to hire someone to build the product, you call it Machine Learning.
- 2 min – explain the difference between traditional programming and machine learning Talk track: This graphic shows the difference in how traditional programming is created versus a machine learning model. In traditional programming you have data and a human built algorithm that go through computation to get an output. Static results are generated based on the programmed logic in the algorithm. In Machine Learning (and specific to supervised machine learning) you have data and the expected output of the data that is put into a computation and a algorithm (model) is created. This is called training your model. Once you have a trained model based on the Features (Data) and Labels (Output) then you can operationalize your model. The production model is used by posting Features (data) to the trained model and an output (label) is predicated based on what it learned from the training data. Now lets look at the model building process in a bit more detail.
- This is the cheatsheet to help understand what models should be used for different problems. I really like this because when starting out a path forward can be the hardest part. You start at the green circle and ask yourself. “What am I trying to predict?” The biggest help here is from the start to the 5 colored boxes to tell you what type of model you are building. This is a guideline not a ultimate truth.
- Within Machine Learning there is Supervised Learning and Unsupervised learning. With Supervised learning you use a dataset with features and labels so it can learn to predict a result based on patterns. Examples of this would be classification and regression models. Classification could be like “cat” or “not cat” and regression is like predicting the value of a home. The above example is showing how to predict the housing class based on demographic information about a person. This is a supervised multiclassification example. Unsupervised learning is when you give the algorithm a dataset (without labels) and have it learn or find the patterns and labels without being explicitly told.
- 3 min – Explain the model building process (keep it brief as you will go into more detail as you build the model in AML Visual Interface) Prepare Data: The first thing you need is a dataset! Then you need to preprocess your data which we will go over in detail in the demo. Train Model: Once you have your prepared data its time to test different machine learning models to see which gets the best results for your data. This is iterative because you may need to change the data and/or the model until you think you have a candidate for the production model. Test Model: Now you have a model that you think is going to perform well and you can test it with unseen data. You will prep your data the same way you processed it for training and then score the labels based on the data provided. This is an iterative process as you may need to go back to the beginning and change how you prepare your data or change your features. Its definitely a fail fast process so don’t overthink each step. Get out what you think will work and iterate through until you get a model that performs good enough on your unseen data. Operationalize Model: Once you have the “chosen one” aka your chosen model. Its time to operationalize it so you can consume it from different applications.
- 1 min – Overview/Decision tree of different machine learning options in Azure Here you can see that you went over the prebuilt model options before this demo. Now we are going to check out the build your own custom model options in azure. We are going to talk about the visual tooling in azure machine learning studio but also take note of the other path/options if you decide to go code-first in the future.