azure machine learning
TRANSCRIPT
Microsoft 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 @ SolidQ
E-mail: [email protected]: @mauridb Blog: http://sqlblog.com/blogs/davide_mauri
Davide Mauri
• 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
What’s that?
MACHINE LEARNING
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!
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
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
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
LANGUAGES
•Most common languages used for machine learning• R• Python
•Less common but on the rise• Julia• Scala•Go• Rust
TOOLS - PYTHON
•Python Packages• Scikit-Learn• SciPy, NumPy, Pandas, Matplotlib, Seaborn
• Jupyter (was: IPython)• Anaconda
•Microsoft Data Science Virtual Machine•Pytools for Visual Studio
TOOLS - R
•R•RStudio•Microsoft Open R Portal•Microsoft R Open (MRO)
•Microsoft Data Science Virtual Machine•Anaconda• https://www.continuum.io/conda-for-r
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/
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
IRIS DATASET
http://www.anselm.edu/homepage/jpitocch/genbi101/diversity3Plants.html
DEMOExperiments On-Premises
On the cloud!
AZURE ML STUDIO
AZUREML STUDIO•www.azureml.com
•Azure ML Studio•Web application (“Workspace”) for developing ML solutions
•Development Process• Experiment• Score• Evaluate• Publish
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/
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!
DEMOExperiments on Azure
AZUREML WEB SERVICES
•Can be created also on-premises with R and Python
DEMOLOB APP Integration:Making it worth for real-life business scenario
QUESTIONS & ANSWERS
TO DO LIST
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