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Age of AzureMachine Learning Revolution
Pablo Maceiras@P_maceiras
.NET Conf UY v2015
Age of AzureMachine Learning Revolution
Pablo Maceiras@P_maceiras
.NET Conf UY v2015
Agenda
Banking!
Desafío de Marketing de Gestión de Campañas
Omni-canal
Omni-Virtual
CustomerProfile
Challenges of applying ML
• Concept Confusion• BI• Data Mining• Machine Learning• Pattern Matching• Big Data• Analytics• Etc.
•ML algorithm selection
• Context set up
• Etc.
Evolve: Part I - Data
• Data Factory
• Data Catalog
• Event Hubs
Data Factory Zoom-In
Evolve: Part II – Big Data
• Azure SQL Data Warehouse
• Azure Data Lake
Evolve: Part III – ML on Data
• See (Face)
• Hear (Speech)
• Read (Text)
Evolve: Part IV – Analyze and Predict
•Machine Learning
• HDInsight (Hadoop)
• Azure Stream Analytics
Evolve: Part IV – Visualize & Act
• Cortana
• Dashboards / Power BI
• Business Scenarios
Evolve: Final – Cortana Analytics
Cortana Analytics
• Predict
• Prescribe• Recommendations • Alerts
• Automate
Real Case Product
IC Campaigns – Segmentation Approaches
+ Rule Matching
+ A/B Testing
+ Machine Learning
IC Campaigns – Challenges
+ Hide model selection from user?
+ Hide parameters from user?
+ Clean and set up data?
What is the Infinity Gauntlet then?• Good Data
• Interpret and analyze results
• Reliability / failure tolerance
• Cost
• Timing
The Dream
Age of AzureMachine Learning Revolution
Pablo Maceiras@P_maceiras
.NET Conf UY v2015