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Azure Machine Learningfrom basic to integration with custom application
Davide Mauri
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About MeMicrosoft SQL Server MVPWorks with SQL Server from 6.5, on BI from 2003Specialized in Data Solution Architecture, Database Design, Performance Tuning, High-Performance Data Warehousing, BI, Big DataPresident of UGISS (Italian SQL Server UG)Regular Speaker @ SQL Server eventsConsulting & Training, Mentor @ SolidQE-mail: [email protected]: @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
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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
• Jupyter (was: IPython)• Anaconda
• Microsoft Data Science Virtual Machine• Pytools for Visual Studio
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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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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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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 Datasethttp://www.anselm.edu/homepage/jpitocch/genbi101/diversity3Plants.html
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DEMOExperiments On-Premises
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SQL Server 2016 and R
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Native support for R in SQL Server 2016!
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DEMOExperiments with R and SQL Server 2016
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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• Part of the “Cortana Analytics Intelligence Suite”
• Development Process• Experiment -> Score -> Evaluate• Publish
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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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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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DEMOExperiments On-Premises
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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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AzureML Web Services• Can be created also on-premises with R and Python
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DEMOExperiments With Web Services
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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 GitHubhttps://github.com/yorek/devweek2016