Presentation giving as part of the Global Azure Bootcamp 2017, April 22, 2017. Subject: one-day hands-on workshop about the Cortana Intelligence Suite.

1. Azure Global Bootcamp
Hands on with Cortana Intelligence Suite
Jan Pieter Posthuma – 22 april 2016

2. Welcome @ Azure Global Bootcamp 2017
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#GlobalAzure
Rubicon - Gast
Welkom@Rubicon

3. Timetable
• 09:00 – Welcome
• 09:30 – Introduction
• 09:45 – Azure Stream Analytics
• 11:15 – Azure Data Lake Store
• 12:30 – Lunch
• 13:15 – Azure Data Lake Analytics
• 14:30 – Azure Machine Learning
• 15:45 – Wrap-up / AzureML UDF in ASA
• 16:00 – Drinks
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4. Who am I?
• Jan Pieter Posthuma – Microsoft Data Consultant
• DataScenarios – Data Consultancy Company
• Architect roles at multiple projects
• Creator of Power BI Custom Visuals
• HierarchySlicer
• Box and Whisker chart
• Contact
• mail@datascenarios.nl
• https://twitter.com/jppp
• https://linkedin.com/in/jpposthuma
• https://github.com/liprec
• https://docs.com/liprec
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5. Prerequisites
• Azure Subscription
(Free signup: https://azure.microsoft.com/pricing/free-trial/)
• Azure ML subscription
(Free signup: https://studio.azureml.net/?selectAccess=true&o=2)
• Visual Studio 2017 (2015) with Azure Data Lake Tools
(2017: via Tools and Extensions;
2015: https://www.microsoft.com/en-
us/download/details.aspx?id=49504)

6. From data to decisions to action
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7. Cortana Intelligence Suite
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8. Hands on labs scenario
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Lab 1 – Ingest Lab 2 – Store and Prepare Lab 3 –Learning
Azure
Stream
Analytics
Azure
Data Lake
Store
Azure
Data Lake
Analytics
Azure
Machine
Learning
Power BI
Close the loop

9. Lab information
• Find Azure Event Hub information here:
https://bit.ly/GAB-NL
https://bit.ly/GAB-NL-HOL
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Azure Stream Analytics
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11. Azure Stream Analytics
• Fully managed, cost effective real-time event processing
engine
• T-SQL like language, SAQL
• Scalable cloud solution
• Input: Azure Event Hubs, Azure Storage Blobs
• Output: Azure Event Hubs, Power BI, Azure Storage Blobs, Azure
Data Lake Store, DocumentDB, Azure SQL DB
• AzureML integration via UDF

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Azure Data Lake Store
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13. Azure Data Lake Store
• Enterprise-wide hyper-scale repository for big data analytic
workloads
• Apache Hadoop file system compatible (HDFS)
• Unlimited storage
• Highly-available: Cloud scale, redundant copies
• Secure: Authentication (AAD), Access control (ACL) and Encryption
(Azure Key Vault)
• All data

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Azure Data Lake Analytics
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15. Azure Data Lake Analytics
• Big data analytics designed to run on top of
Azure Data Lake Store
• Dynamic scaling: cloud scale, dynamically provisions resources
• U-SQL: simple and familiar, powerful, and extensible (via .NET)
• Job (query, priority and max parallelism) duration defines price
• Works also with: Azure Blob Storage, Azure SQL DB

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Azure Machine Learning
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17. Azure Machine Learning
• Machine Learning:
‘the study of systems that can learn from data’
• MLStudio: ‘IDE’ for designing, training and validating models
• Data consumption via upload or Azure Storage (no ADLS yet)
• Operationalizing via WebService integration
• Retraining model via API

18. Supervised learning
• Infer a target function from
labeled dataset
• Example algoritms:
• Classification – identifying which
categories observation belongs
• Regression – estimating the
relationships among variables
• Dataset split needed
• Training
• Validation
• Test
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Data Target
Model
Test data Target

19. Unsupervised learning
• Identify naturally occurring
patterns in data
• Example algorithms:
• Clustering – grouping a set of
objects based on similarity into
clusters
• Outlier detection – identification
of items which do not conform to an
expected pattern
• No data split needed
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Data (no labels)
Model
Naturally
occurring
hidden
structure

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Drinks
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