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Bdug introduction to azure machine learning

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Bdug introduction to azure machine learning

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Bdug introduction to azure machine learning

  1. 1. WHO AM I? ■ SolutionsArchitect at PwC Belgium ■ Office Development MVP - 4Years. ■ 18 years of experience as developer ■ Azure Certified SolutionsArchitect ■ Founder: Brussels Developers User Group ■ Twitter: @levalencia ■ Blog: www.luisevalencia.com
  2. 2. Social networks #brusselsdevug @brusselsdevug /brusselsdevug linkedin.com/groups/13826566/ meetup.com/BrusselsDevelopersUserGroup
  3. 3. Hype 4
  4. 4. HandWringing* • Bad • Creepy • Will kill Everyone • Will take all our jobs • Dangerous • Should be banned Terminator, Skynet, Somebody think of the children! *The excessive display of concern or distress. 5
  5. 5. 6 I don't have a problem with anyone who's critical or sceptical about this stuff. I want you to be critical and sceptical; that's fine, and there are ethical concerns
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  7. 7. 8 Machine learning is not a new way to solve familiar problems
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  9. 9. Machine learning is a new way to solve new kind of problems 10
  10. 10. 11 That's a little cryptic, and when I say new kinds of problems, I don't mean problems you have to invent or things you didn't even realize were problems, or entirely new categories in the problem space. No, more often these are situations that you're already perfectly aware of.You might not even call them problems.You might think of them as business decisions or issues or tasks, but where in the past you might've assumed that computers just couldn't help you or couldn't help you that much. Like what?
  11. 11. How much is a good price for this new product? How often should we send marketing emails? Are there indications our website is about to be attacked? Is the discussion positive or negative? 12
  12. 12. But,What really is Machine Learning then?
  13. 13. We take existing data, Analyse it to identify patterns, Then use the results To make better predictions About new data 14
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  23. 23. Positive Samples Negative Samples Classification 24
  24. 24. Incoming email - Classify as spam or not spam. Website activity - Classify it as high value customer or not. Incoming attachment - Classify it as a contract or not a contract Classification Samples 25
  25. 25. What is AI?
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  28. 28. Workspaces A workspace defines the boundary for a set of related machine learning assets.You can use workspaces to group machine learning assets based on projects, deployment environments (for example, test and production), teams, or some other organizing principle.The assets in a workspace include: ■ Compute targets for development, training, and deployment. ■ Data for experimentation and model training. ■ Notebooks containing shared code and documentation. ■ Experiments, including run history with logged metrics and outputs. ■ Pipelines that define orchestrated multi-step processes. ■ Models that you have trained: A model is the result of a Azure Machine learning training Run or some other model training process outside of Azure
  29. 29. Workspaces
  30. 30. The Azure resources created alongside a workspace include: ■ A storage account - used to store files used by the workspace as well as data for experiments and model training. ■ An Application Insights instance, used to monitor predictive services in the workspace. ■ An Azure KeyVault instance, used to manage secrets such as authentication keys and credentials used by the workspace. ■ Virtual Machines, and their associated virtual hardware resources, used to provide compute for notebook development in the workspace. ■ A container registry, used to manage containers for deployed models
  31. 31. Demos! – Create Machine LearningWorkspace – Compute ■ Jupyter ■ JupyterLab ■ RStudio – Notebooks ■ Jupyter ■ JupyterLab ■ Inline – Datasets – Using a Dataset from Python – Datastores – Models – Endpoints
  32. 32. ■ Just a 3% improvement in detecting gift card fraud resulted in $40 million loss avoidance ■ Does not have to be perfect to be valuable ■ Microsoft’sVision for ML Make machine learning accessible to every enterprise, data scientist, developer, information worker, consumer, and device anywhere in the world Value of ML
  33. 33. ■ Credit scoring first used by mail order business in 1950’s ■ 3 Fundamental Benefits – Speed – evaluate millions of customers in seconds – Accuracy - more accurate than humans – about 20-30% – Consistency – a model will always generate same prediction given same set of data – even a competent human expert will not depending on time of day, mood or whether hungry or not – lots of evidence of this consistency problem Really Focused on Predictive Analytics
  34. 34. ■ Identifying people who don’t pay their taxes ■ Calculating probability of having a stroke in next 10 years ■ Spotting which credit card transactions are fraudulent ■ Selecting suspects in criminal cases ■ Deciding which candidate to offer a job to ■ Predicting how likely it is that a customer will become bankrupt ■ Predicting which customers are likely to defect to rival phone plan when their contract reaches its end ■ Determining what books, music and films you are likely to purchase next ■ Forecasting life expectancy Some Uses of Predictive Analytics
  35. 35. ■ https://docs.microsoft.com/en-us/learn Where to learn more
  36. 36. Thank you https://www.luisevalencia.com Twitter: @levalencia
  37. 37. Social networks #brusselsdevug @brusselsdevug /brusselsdevug linkedin.com/groups/13826566/ meetup.com/BrusselsDevelopersUserGroup