Adventures in Azure Machine Learning from NE Bytes
•
2 likes•436 views
Presentation given at NE Bytes .Net Developer User Group on 16th March 2016 at Campus North in Newcastle upon Tyne.
Recommended
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to Adventures in Azure Machine Learning from NE Bytes
Similar to Adventures in Azure Machine Learning from NE Bytes (20)
More from Derek Graham
More from Derek Graham (12)
Recently uploaded
Recently uploaded (20)
Adventures in Azure Machine Learning from NE Bytes
- 2. derek graham! principal developer @sage! special product responsibility for Azure bits
- 6. Machine learning is not telling computers what to do but letting them learn from examples or past experience
- 8. Which means you can… • Analyse historic or current data • Find patterns and trends • Make predictions about future events
- 13. Project Adam
- 18. Azure Machine Learning • A "new" cloud-based service from Microsoft • Integrates with existing Cloud technologies • Use ready-made algorithms • Program custom algorithms tuned to your problem • You can evaluate it for free
- 22. • Browser based • Drag n Drop • Flowchart-y • Example data sets • Use R or Python • Excellent intro wizard ML Studio
- 24. • Import data • Filter and aggregate data • Create machine learning models • Run experiments • Publish finished model Provides tools to:
- 26. The Learning Process • Define a problem you want to solve • Design a solution • Experiment! ! • Identify your data • Train the model with the data • Evaluate against expected results (speed and accuracy) • Adapt data or algorithm (or both) • Repeat ! • Save the best model • Publish • Run with live data
- 27. Proof!
- 28. Imagine… • "Business" Software • Azure hosted • PaaS • Multi-tenanted
- 29. Open-ended Workflow • Monday Morning Login • Friday Reports • In between? • Weekends? • Holidays?
- 30. Balancing • User demand • User experience • Compute resources • Cost
- 31. Scaling • Instances auto-scale based on the CPU% metric using Azure’s standard scaling model. • Azure standard scaling is slow • Once auto scaler notices we need more capacity, the demand has often disappeared! • Not a good user experience
- 33. Experiment • Customer use is not regular... • ...but, is it predictable?
- 34. Hackathon! • Can we build a better autoscaler? • Spin-up before high demand • Tear-down when idle • Better Cost vs UX
- 36. Requirements • What will "we" need on a given date or time? • Do "we" need to take action now to compensate for what will happen in 20 minutes time? • Number of instances • Predicted CPU
- 37. Best Predictor of Demand? • Sessions? • Instance Memory Use? • Instance CPU?
- 39. Table Storage Diagnostics • Too slow • Purging • ML queries all or nothing • ML Data Reader stops after 4GB • GB !!!! • ML times-out after ~3 Hours
- 40. CPU
- 41. Event Hubs • Application log sink • Low overhead • Highly scalable • Time-based • Disposable
- 43. Neural Net Experiments ! • Feed Forward NN • Written using R libraries • Good predictor for 10-20 minute window • Too inaccurate after that • Best compromise between precision and speed • Recurrent NN better at forecasting • RNN execution time too long • Need to reduce data to optimal subset
- 44. Stream Analytics ! • Real-time data analysis • Fast • Sql-like syntax • Range of inputs and outputs • Interesting development
- 50. Anomalies • Dev Process is painful • Syntax Errors • “Test” Import Behaviour • Starting and Stopping and Starting and Stopping
- 52. Compromise
- 53. Closing the Loop
- 54. Publish… • ReST Web service • Client Worker Role • Management Service API
- 55. …& Be Damned • Too much data crashes model • Fine in ML Studio • 500 • Out of memory?
- 57. Finished!
- 59. Result!
- 62. What we learned
- 63. Bugs • We were pushing the environment quite hard • YMMV • ML studio has bugs • Parallel tasks !Parallel • ML portal missing functionality preventing it being production ready
- 64. #DevOps • Sharing models is "public" - Gallery • No export support • No support (yet) for model deployment • Still Drag n Drop • PowerShell for EventHubs and Stream Analytics
- 65. Machine Learning • Parallel R processing library would help • Finding an appropriate solution often requires a data science specialist • Solution is only as good as your data • You may need to compromise on accuracy for speed • Cost • Hosting • Each call to the service
- 67. E-Book • Microsoft Azure Essentials: Azure Machine Learning • Download from: https://mva.microsoft.com/ebooks
- 68. Titanic • Jennifer Marsman - https:// blogs.msdn.microsoft.com/jennifer/2016/02/19/ using-azure-machine-learning-to-predict-who-will- survive-the-titanic/ • Data Science! https://www.kaggle.com/ • Amy Nicholson @AmyKateNicho https:// blogs.technet.microsoft.com/amykatenicho/
- 69. #DevOps • https://azure.microsoft.com/en-gb/documentation/articles/event- hubs-programming-guide/ • https://azure.microsoft.com/en-gb/documentation/articles/service- bus-event-hubs-manage-with-ps/ • https://azure.microsoft.com/en-us/documentation/articles/stream- analytics-dotnet-management-sdk/ • https://azure.microsoft.com/en-us/documentation/articles/stream- analytics-monitor-and-manage-jobs-use-powershell/
- 70. Questions ?