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Azure Machine LearningA full journeyDavide Mauridmauri@solidq.com@mauridb

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Sponsors

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Davide Mauri

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 Tool & Languages

Experimenting On-Premises Ipython & R

Azure Machine Learning AzureML Studio Notebooks

Integrating AzureML in custom applications Creating AzureML Services with Python and R

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MACHINE LEARNINGWhat’s that?

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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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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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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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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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Languages

Most common languages used for machine learning R Python

Less common but on the rise Julia Scala Go Rust

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Tools - Python

Python Packages Scikit-Learn SciPy, NumPy, Pandas, Matplotlib, Seaborn

IPython (Jupyter) Anaconda

Pytools for Visual Studio

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Tools - R

R RStudio Anaconda

https://www.continuum.io/conda-for-r

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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

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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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IRIS Dataset

http://www.anselm.edu/homepage/jpitocch/genbi101/diversity3Plants.html

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EXPERIMENTSOn-Premises

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AZURE ML STUDIOOn the cloud!

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AzureML Studio

www.azureml.com

Azure ML Studio Web application (“Workspace”) for developing ML solutions

Development Process Experiment Score Evaluate Publish

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EXPERIMENTSAzure

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AzureML Web Services

Can be created also on-premises with R and Python

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LOB APP INTEGRATIONMaking it worth for real-life business scenario

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Evaluations

Don’t forget to compile evaluations form here http://speakerscore.com/sqlsat454

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THANKS!

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