adventures in azure machine learning from ne bytes

Adventures!in Azure Machine

Learning@deejaygraham

derek graham!principal developer @sage!

special product responsibility for Azure bits

Sorry!!live coding!

live portall-ing

Machine learning is not telling

computers what to do but letting them

learn from examples or past experience

Which means you can…

• Analyse historic or current data • Find patterns and trends • Make predictions about future events

Project Adam

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

http://studio.azureml.net

• Browser based • Drag n Drop • Flowchart-y • Example data sets • Use R or Python • Excellent intro wizard

ML Studio

• Import data • Filter and aggregate data • Create machine learning models • Run experiments • Publish finished model

Provides tools to:

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

Proof!

Imagine…• "Business" Software

• Azure hosted

• PaaS

• Multi-tenanted

Open-ended Workflow• Monday Morning Login

• Friday Reports

• In between?

• Weekends?

• Holidays?

Balancing• User demand

• User experience

• Compute resources

• Cost

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

Experiment• Customer use is not regular...

• ...but, is it predictable?

Hackathon!• Can we build a better autoscaler?

• Spin-up before high demand

• Tear-down when idle

• Better Cost vs UX

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

Best Predictor of Demand?

• Sessions?

• Instance Memory Use?

• Instance CPU?

Table Storage Diagnostics• Too slow

• Purging

• ML queries all or nothing

• ML Data Reader stops after 4GB

• GB !!!!

• ML times-out after ~3 Hours

CPU

Event Hubs• Application log sink

• Low overhead

• Highly scalable

• Time-based

• Disposable

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

Stream Analytics!

• Real-time data analysis

• Fast

• Sql-like syntax

• Range of inputs and outputs

• Interesting development

Anomalies• Dev Process is painful

• Syntax Errors

• “Test” Import Behaviour

• Starting and Stopping and Starting and Stopping

Compromise

Closing the Loop

Publish…• ReST Web service

• Client Worker Role

• Management Service API

…& Be Damned• Too much data crashes model

• Fine in ML Studio

• 500

• Out of memory?

Finished!

Result!

What we learned

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

#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

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

References

http://studio.azureml.net/

E-Book• Microsoft Azure Essentials: Azure Machine

Learning

• Download from: https://mva.microsoft.com/ebooks

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/

#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/

Questions ?