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