Bdug introduction to azure machine learning

2. 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

3. Social networks
#brusselsdevug
@brusselsdevug
/brusselsdevug
linkedin.com/groups/13826566/
meetup.com/BrusselsDevelopersUserGroup

4. Hype
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5. 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.
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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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Machine learning is not a new way to solve familiar
problems

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10. Machine learning is a new way to solve new kind
of problems
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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?

12. 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

13. But,What
really is
Machine
Learning then?

14. We take existing data,
Analyse it to identify patterns,
Then use the results
To make better predictions
About new data
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24. Positive Samples Negative Samples
Classification
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25. 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
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26. What is AI?

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28. AZURE MACHINE
LEARNING SERVICE

30. 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

31. Workspaces

32. 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

33. Demos!
– Create Machine LearningWorkspace
– Compute
■ Jupyter
■ JupyterLab
■ RStudio
– Notebooks
■ Jupyter
■ JupyterLab
■ Inline
– Datasets
– Using a Dataset from Python
– Datastores
– Models
– Endpoints

34. ■ 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

35. ■ 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

36. ■ 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

37. ■ https://docs.microsoft.com/en-us/learn
Where to learn more

38. Thank you
https://www.luisevalencia.com
Twitter: @levalencia

39. Social networks
#brusselsdevug
@brusselsdevug
/brusselsdevug
linkedin.com/groups/13826566/
meetup.com/BrusselsDevelopersUserGroup