1. Power BI Integration with Azure Machine Learning
Chad Dotzenrod
Analytics & Cloud Evangelist at SWC Technology Partners

2. Meet Your Presenter: Chad Dotzenrod
Analytics & Cloud Evangelist at SWC Technology Partners
PUG Membership: Greater Chicago Power BI User Group
20 Years of work in Data and Analytics
Education: NDSU & DeVry Keller
Fun facts! Avid fisherman and competitive BBQ contestant
Follow me on
Connect with me on LinkedIn: chaddotzenrod

3. Agenda Quick Audience Survey Why Is This Important?
Integration Scenarios
Review Azure Machine Learning Workbench
Demo
Q&A

4. Audience Survey

6. Why is Azure Machine Learning Integration Important?
Start with the right Approach - Pop Culture Reference - Star Wars Death Star, etc

7. Making Decisions with Data
Descriptive > Predictive > Prescriptive
Command and Control via Power BI
Trust but Verify
Understanding what the algorithms, models, and machines are doing to us!
Use Power BI to question everything happening with the data
Prove the ROI
Show and Tell a story that is impactful to the organization
Steer clear of Data Science jargon when presenting to citizens!
Lift charts, AUC graphs, and False Positive rates make sense to data scientists and statisticians but not to the business

8. Microsoft TDSP Process Data Science is a process! No Shortcuts!
Team Data Science Process

9. Who is Solving What? Use Case Automotive Manufacturing Consumer
Finserve
Energy
Pharma
Public
Media
Logistics
Real-time
Optimization
Strategic Optimization
Predictive Analytics
Predictive Maintenance
Radical Personalization
Trend/Anomaly Discovery
Forecasting
Asking the Questions: You can’t deploy Power BI successfully if you only talk to IT and same goes if you only talk to the business
Pragmatism: Know your users. Understand their journey when it comes to answering questions with data
Competition: Are there other BI tools in place, Can the users break from Excel, Are there legacy reports that users depend on? Will they change
Source: McKinsey – The Age of Analytics Report, Dec 2016

10. Integration Scenarios
House Marketing is fighting House Accounting, they don't see the White Walkers, you don't want the wall. WINTER IS COMING, WINTER IS HERE - One unified 7 Department Kingdoms…

11. Integration Scenarios
Plentiful Options
Batch
Real-Time
On-Demand
Complexity & Skillsets
Latency/Data Prep/Cost
SQL/R/Python/Azure

14. Review Azure Machine Learning Toolset
Security - Walking Dead –   Protect the wrong people from the wrong things… that's why we have role based security.
Choose the right tool  - not a bow and arrow, you need a machete. Not just a firewall, you need xyz.

15. Azure Machine Learning Studio
Citizen Data Science Tool
Visual Editor
Gallery Support
Reasonable Cost
Pure PaaS

16. Azure Machine Learning Studio Details
What skillset is needed to get started?
How much does it cost?
Deployment Options
Integration with Excel, LOB Apps, and Power BI
Power BI is useful in telling the model story
* Studio is meant for citizen data scientists and Workbench is for Professionals. Think of one as a visual IDE and the Other as a code based IDE. Both can get the same results but the latter has more options for configuration and customization
First two users experiment accounts are free, You will be charged for model deployment compute if in cloud. Additional experiment accts are $50/month preview and likely $100/month GA

17. Azure Machine Learning Workbench
Professional Data Science Tool
Enhanced Data Preparation
Model Experimentation
Model Management
Bridges
On-Premise, Hybrid, & Cloud

18. AML Workbench Details
How is this different from AML PaaS?
How much does it cost?
Deployment Targets:
Docker Containers (Local or Azure)
Virtual Machines (Azure)
SQL Server Machine Learning Server (Local or Azure)*
Azure IoT Edge*
Integration with Visual Studio AI Tools
Excellent Companion Tool for Power BI Solution Builders!
* Studio is meant for citizen data scientists and Workbench is for Professionals. Think of one as a visual IDE and the Other as a code based IDE. Both can get the same results but the latter has more options for configuration and customization
First two users experiment accounts are free, You will be charged for model deployment compute if in cloud. Additional experiment accts are $50/month preview and likely $100/month GA
*These deployment targets are not currently in preview yet but are on the roadmap

19. Power BI Challenges for AML Workbench
Data Profiling is not easy enough and many don’t know how to approach
Working with “Big Data” can be cumbersome and slow
Cleansing capabilities are excellent but not as rich as AMLW.
No seamless Integration with GIT or other code repositories
Power BI Adoption Speedbumps:
Hard data challenges (complicated transforms) might have to go to the ETL Team
Performance data challenges might go to Database Team (EDW, Cubes, In-memory)
Functional challenges – I can’t do X with PBD and I don’t know how to write R Scripts
* Studio is meant for citizen data scientists and Workbench is for Professionals. Think of one as a visual IDE and the Other as a code based IDE. Both can get the same results but the latter has more options for configuration and customization
First two users experiment accounts are free, You will be charged for model deployment compute if in cloud. Additional experiment accts are $50/month preview and likely $100/month GA

20. Demo Demo/Overview of AML Workbench
Demo of Power BI integration with AML.

21. Thank You for Attending!
Don’t forget to join your local PUG
to enjoy year-round networking and learning.
Twitter: @cdotzenrod
LinkedIn: chaddotzenrod