Code camp iasi silviu niculita - machine learning for mere mortals with azure ml
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Transcript of Code camp iasi silviu niculita - machine learning for mere mortals with azure ml
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Vision Analytics
Recommenda-
tion engines
Advertising
analysis
Weather
forecasting for
business
planning
Social network
analysis
Legal
discovery and
document
archiving
Pricing analysis
Fraud
detection
Churn
analysis
Equipment
monitoring
Location-based
tracking and
services
Personalized
Insurance
Machine learning & predictive analytics are core capabilities that are needed throughout your business
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• Automated prediction is
core
• Lots of past data already
available
• Magic numbers in current
prediction system
Yes
• Prediction is small part of
experience
• No past data available
• Many business-rules
govern the experience
• Predictions do not have a
predictable pattern
No
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Import
Data
Build a
Model
Turn model
to API
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Supervised Learning
Unsupervised Learning
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Data Science is far too complex today
• Access to quality ML algorithms, cost is high.
• Must learn multiple tools to go end2end, from data acquisition, cleaning and prep, machine learning, and experimentation.
• Putting a model into production is time consuming.
This must get simpler, it simply won’t scale!
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Azure Portal
Azure Ops Team
ML Studio
Data Scientist
HDInsight
Azure Storage
Desktop Data
Azure Portal &
ML API service
Azure Ops Team
PowerBI/DashboardsMobile AppsWeb Apps
ML API service Developer
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Truth
true falseGuess
positive
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =𝑡𝑝
𝑡𝑝 + 𝑓𝑝
negative
𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑟𝑒𝑐𝑎𝑙𝑙 =𝑡𝑝
𝑡𝑝 + 𝑓𝑛𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑡𝑝 + 𝑡𝑛
𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛
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Reduce complexity to broaden participation
Features and Benefits
• Accessible through a web browser, no software to install;
• Collaborative work with anyone, anywhere via Azure workspace
• Visual composition with end2end support for data science workflow;
• Best in class ML algorithms;
• Extensible, support for R and Python.
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Features and Benefits
• Immutable library of models, search discover and reuse;
• Rapid experimentation to create a better model
• Rapidly try a range of features, ML algorithms and modeling strategies;
• Quickly deploy model as Azure web service to our ML API service.
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Train Test
Use 80% Use 20%
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http://channel9.msdn.com/Events/TechEd/Europe/2014/DBI-B218
http://channel9.msdn.com/Events/TechEd/Europe/2014/CDP-B240
http://channel9.msdn.com/Events/TechEd/Europe/2014/DBI-B321
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http://www.theplatform.net/2015/04/10/cloudy-machine-learning-for-the-masses/
http://www.zdnet.com/article/cloud-machine-learning-wars-heat-up/
http://www.infoworld.com/article/2911946/machine-learning/the-cloud-is-finally-making-machine-learning-practical.html
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