In this session, Davide will explore Azure ML from the inside out. After a gentle approach on Machine Learning, we’ll see the Microsoft offering in this field and all the feature it offers, creating a simple yet 100% complete Machine Learning solution. We’ll start from something simple and then we’ll also move to some more complex topics, such as the integration with R and Python, IPython Notebook until the Web Service publishing and usage, so that we can integrate the created ML solution with batch process or even use it in real time with LOB application. All of this sound cool to you, yeah? Well it is, since with ML you can really give that “something more” to your customers or employees that will help you to make the difference. Guaranteed at 98.75%!

1. Azure Machine Learning
from basic to integration with custom application
Davide Mauri
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About Me
Microsoft SQL Server MVP
Works with SQL Server from 6.5, on BI from 2003
Specialized in Data Solution Architecture, Database Design, Performance
Tuning, High-Performance Data Warehousing, BI, Big Data
President of UGISS (Italian SQL Server UG)
Regular Speaker @ SQL Server events
Consulting & Training, Mentor @ SolidQ
E-mail: dmauri@solidq.com
Twitter: @mauridb
Blog: http://sqlblog.com/blogs/davide_mauri/default.aspx

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Agenda
• Machine Learning, what’s that?
• Supervised & Unsupervised methods
• Tools & Languages
• Experimenting On-Premises
• IPython & R
• SQL Server 2016 and R
• Azure Machine Learning
• AzureML Studio
• Jupyter Notebooks
• Integrating AzureML into custom applications

4. Machine Learning
What’s that?
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5. Machine Learning
• Algorithms that learn from data
• Nothing really new from a scientific point of view
• "Field of study that gives computers the ability to learn without being
explicitly programmed“ - 1959, Arthur Samuel
• Requires *a lot* of compute power (even for not-so-big-data)
• Azure, here we come! 
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6. Machine Learning
• Very useful for
• Identify unknown and complex pattern
• Identify hidden correlations
• Automatically classify data
• Predict future trend and/or values basing on past knowledge
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7. Machine Learning
• Thanks to the cloud it’s now possible to integrate ML Algorithms into
Line-Of-Business applications
• Choose the algorithm
• Train it
• Expose as a RESTful Web Service
• Call it from you App
• You’re Happy 
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8. Machine Learning
• Two main categories (but sometimes are even divided in up to five
categories!)
• Supervised
• Unsupervised
• Supervised: humans (usually) teach to algorithms what is the
expected result
• Unsupervised: algorithms tries to autonomously identify patterns and
rules in given dataset
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9. Languages
• Most common languages used for machine learning
• R
• Python
• Less common but on the rise
• Julia
• Scala
• Go
• Rust
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10. Tools - Python
• Python Packages
• Scikit-Learn
• SciPy, NumPy, Pandas, Matplotlib, Seaborn
• Jupyter (was: IPython)
• Anaconda
• Microsoft Data Science Virtual Machine
• Pytools for Visual Studio
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11. Tools - R
• R
• Microsoft Open R Portal
• Microsoft R Open (MRO)
• RStudio
• R Tools for Visual Studio
• Microsoft Data Science Virtual Machine
• Anaconda
• https://www.continuum.io/conda-for-r
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12. Datasets
• To learn ML, sample and well-known datasets are needed
• Here some places where nice Datasets can be found
• http://archive.ics.uci.edu/ml/datasets.html
• http://www.kdnuggets.com/datasets/index.html
• http://homepages.inf.ed.ac.uk/rbf/IAPR/researchers/MLPAGES/mldat.htm
• https://en.wikipedia.org/wiki/Data_set#Classic_datasets
• https://mran.revolutionanalytics.com/documents/data/
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13. IRIS Dataset
• 150 instances of Iris Flowers
• 3 classes: Virginica, Versicolor, Setosa
• 4 features: Sepal Width & Length, Petal Width & Length
• One of the most used for educational purposes
• Simple, but….
• Un class is linearly separable
• Other two classes are NOT linearly separable
• Available at UC Irvine Machine Learning Repository
• http://archive.ics.uci.edu/ml/datasets/Iris
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14. IRIS Dataset
http://www.anselm.edu/homepage/jpitocch/genbi101/diversity3Plants.html
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DEMO
Experiments On-Premises

16. SQL Server 2016 and R
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DEMO
Experiments with R and SQL Server 2016

18. Azure ML Studio
On the Cloud!
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19. AzureML Studio
• www.azureml.com
• Azure ML Studio
• Web application (“Workspace”) for developing ML solutions
• Part of the “Cortana Analytics Intelligence Suite”
• Development Process
• Experiment -> Score -> Evaluate
• Publish
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20. AzureML Studio
• “Democratize Machine Learning”
• Free Tier Available
• 10 GB Storage Space
• 1h max experiment duration
• Staging Web API
• Standard Tier
• Costs per “Seat”, Studio and API Usage
• https://azure.microsoft.com/en-us/pricing/details/machine-learning/
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21. AzureML Studio
• Fully Interactive Environment
• Fully Integrated with Azure Ecosystem, but not only that 
• Very easy to use external data sources
• Support Jupyter/IPython Notebooks!
• Even more Interative! 
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DEMO
Experiments On-Premises

23. AzureML Web Services
• Once the experiment has been created, tested and validated
• NOTE! AzureML Web Services *does not* support CORS right now
• So it’s ok to call from a server-side script
• If you want to call it directly from Javascript you need to go through Azure
Management API
• And configure the Policy to allow CORS
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24. AzureML Web Services
• Can be created also on-premises with R and Python
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DEMO
Experiments With Web Services

26. What’s more
• In addition to the “usual” Machine Learning objects Azure offers
specialized API and the “Cognitive Services”
• https://www.microsoft.com/cognitive-services
• https://gallery.cortanaintelligence.com/machineLearningAPIs
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Thanks!
Questions?

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Demos available on GitHub
https://github.com/yorek/devweek2016