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Azure ML: from basic to integration with custom applications
The document discusses Azure Machine Learning and its integration with custom applications, covering fundamentals like machine learning categories (supervised and unsupervised), common programming languages (R, Python), and various tools. It features a detailed agenda including hands-on demos with SQL Server, Azure ML Studio, and web services. Additionally, it highlights resources for datasets and encourages participation in discussions via social media during the DevWeek event.
Azure ML: from basic to integration with custom applications
- 1. Azure Machine Learning frombasic to integration with custom application Davide Mauri Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 2. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek 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
- 3. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek 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? Jointhe conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 5. Machine Learning • Algorithmsthat 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! Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 6. Machine Learning • Veryuseful for • Identify unknown and complex pattern • Identify hidden correlations • Automatically classify data • Predict future trend and/or values basing on past knowledge Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 7. Machine Learning • Thanksto 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 8. Machine Learning • Twomain 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 9. Languages • Most commonlanguages used for machine learning • R • Python • Less common but on the rise • Julia • Scala • Go • Rust Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 12. Datasets • To learnML, 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/ Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 13. IRIS Dataset • 150instances 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 14. IRIS Dataset http://www.anselm.edu/homepage/jpitocch/genbi101/diversity3Plants.html Join theconversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 15. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek DEMO Experiments On-Premises
- 16. SQL Server 2016and R Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 17. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek DEMO Experiments with R and SQL Server 2016
- 18. Azure ML Studio Onthe Cloud! Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 20. AzureML Studio • “DemocratizeMachine 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/ Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 21. AzureML Studio • FullyInteractive Environment • Fully Integrated with Azure Ecosystem, but not only that • Very easy to use external data sources • Support Jupyter/IPython Notebooks! • Even more Interative! Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 22. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 24. AzureML Web Services •Can be created also on-premises with R and Python Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 25. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek DEMO Experiments With Web Services
- 26. What’s more • Inaddition 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 Join the conversation on Twitter: @DevWeek // #DW2016 // #DevWeek
- 27. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek Thanks! Questions?
- 28. Join the conversationon Twitter: @DevWeek // #DW2016 // #DevWeek Demos available on GitHub https://github.com/yorek/devweek2016