Power BI has become an increasingly important data analytics tool. This presentation focuses on the advanced analytics options currently available in Power BI. Attendees to this talk will see:
· Microsoft’s perspective on advanced analytics development: the Team Data Science Process
· What the general options are for advanced analytics on Azure
· What the specific native advanced analytics capabilities are in Power BI
· Some ideas on pairing Power BI with other technologies in advanced analytics architectures
16. Local machine
Scale up to DSVM
Scale out with Spark on HDInsight
Azure Batch AI (Coming Soon)
ML Server
Azure Machine Learning - Experimentation
A ZURE ML
EXPERIMENTATION
Command line tools
IDEs
Notebooks in Workbench
VS Code Tools for AI
20. Visual Studio Tools for AI
Visual Studio extension with deep
integration to Azure ML
End to end development
environment, from new project
through training
Support for remote training
Job management
On top of all of the goodness of
Visual Studio (Python, Jupyter, Git,
etc)
21. Azure Machine Learning Studio
Platform for emerging data scientists to
graphically build and deploy experiments
• Rapid experiment composition
• > 100 easily configured modules for
data prep, training, evaluation
• Extensibility through R & Python
• Serverless training and deployment
Some numbers:
• 100’s of thousands of deployed models
serving billions of requests
22. Machine Learning & AI Portfolio
When to use what?
What engine(s) do you want
to use?
Deployment target
Which experience do you
want?
Build your own or consume pre-
trained models?
Microsoft
ML & AI
products
Build your
own
Azure Machine Learning
Code first
(On-prem)
ML Server
On-
prem
Hadoop
SQL
Server
(cloud)
AML services (Preview)
SQL
Server
Spark Hadoop Azure
Batch
DSVM Azure
Container
Service
Visual tooling
(cloud)
AML Studio
Consume
Cognitive services, bots