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Organized Globally, Held Locally
GLOBAL POWER
PLATFORM
BOOTCAMP
GLOBAL POWER
PLATFORM
BOOTCAMP
Organized Globally, Held Locally
@GPPBootcampBLR #GPPBootcampBLR #GlobalPowerPlatformBootcamp
Mitul Rana
Let’s integrate natural language in your
Power BI reports and Ask a question on your data.
@mitul_rana
Platform Specialist, Trelleborg
Samik Roy
@roy_samik
Senior Developer, Born in the Cloud
@GPPBootcampBLR
#GPPBootcampBLR
#GlobalPowerPlatformBootcamp
Sponsors
Sponsors
Agenda
• Investment areas
• Personas and capabilities
Overview of AI in Power BI
• AI visualizations
• Natural language capabilities
End User Insights
• Automated ML
• Cognitive services
Analyst toolkit
AI in Power BI
Sentiment Analysis
Key Phrase Extraction
Create ML models
Explore predictionsPython Integration
R IntegrationExtend with Azure ML
Integrate into reports
AI Visualizations
Natural Language
AI in Power BI
Sentiment Analysis
Key Phrase Extraction
Create ML models
Explore predictionsPython Integration
R IntegrationExtend with Azure ML
Integrate into reports
AI Visualizations
Natural Language
Demo: AI Visuals
AI in Power BI
Sentiment Analysis
Key Phrase Extraction
Create ML models
Explore predictionsPython Integration
R IntegrationExtend with Azure ML
Integrate into reports
AI Visualizations
Natural Language
AI in Power BI
Sentiment Analysis
Key Phrase Extraction
Create ML models
Explore predictionsPython Integration
R IntegrationExtend with Azure ML
Integrate into reports
AI Visualizations
Natural Language
Demo: Automated ML
Key Takeaways – AutoML
POWER BI + AUTOMATED ML ENABLES YOU TO:
AUTOMATE MOST OF THE STANDARD MACHINE
LEARNING PRACTICES IN A NON-INTRUSIVE YET
TRANSPARENT WAY
AI IN POWER BI IS NOT ONLY ABOUT ACHIEVING GREAT
ACCURACY BUT ABOUT EASY TRANSLATING THIS
ACCURACY TO BUSINESS IMPACT
Key Takeaways – AutoML
Automated ML in Power BI Automated ML in Azure
Designed for the business analyst/BI professional Designed for for the data scientist/data engineer
Skillset required is Power Query and Power BI
dataflows (OOB point and click functions, M
code)
Skillset required is Azure, Python and Jupyter notebooks
ML model lives completely inside Power BI and
can only be used by Power BI
ML model lives in Azure and can be consumed as a
webservice by lots of different applications (including Power
BI)
Requires Power BI Premium and utilizes capacity
Requires an Azure subscription and utilizes Azure
consumption
Demo:
Cognitive
Services
Key Takeaways – Cognitive Services
Cognitive services in Power BI Cognitive services in Azure
Designed for the business analyst/BI professional Designed for for the data scientist/data engineer
Skillset required is Power Query and Power BI
dataflows (OOB point and click functions, M code)
Skillset required is Azure, coding language (C#, Java, Node.js,
Python)
Lives completely inside Power BI and can only be used
by Power BI
Live in Azure and can be consumed as a webservice by lots of
different applications
Require Power BI Premium and utilizes capacity
Require an Azure subscription and utilizes Azure consumption
Session takeaways
Power
BI + AI
enables:
Findings insights seamlessly and interacting with
data in a natural way using language
Gives analysts the opportunity to
enrich their data using AI and build
their own machine learning model
without writing any code
Extension points for data scientists to plug in their
Python/R scripts or to bring in machine learning
models they created elsewhere
Call to Action
• Try some of our quick start guides to get started
with Automated ML, Cognitive Services and
Azure ML on dataflows:
• https://docs.microsoft.com/en-us/power-
bi/service-machine-learning-automated
• Download the latest Power BI Desktop to try AI
Visuals
• https://powerbi.microsoft.com/en-us/desktop/
• Get in touch with the team to talk to us about
your use cases, pain points, roadmap feedback
(seriously we love hearing from you ):
• Mitul.rana@gmail.com
• Samik.n.roy@gmail.com
Any
Questions?
Helpusgrow!
@GPPBootcampBLR #GPPBootcampBLR
#GlobalPowerPlatformBootcamp
Thanks!
BuildPowerfulreportsusingDAXfunctionsin
MicrosoftPowerBI
BijayKumar Sahoo
(MVP) Co-FounderTSInfoTechnology

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GPPB Natural Language PowerBI

  • 1. Organized Globally, Held Locally GLOBAL POWER PLATFORM BOOTCAMP GLOBAL POWER PLATFORM BOOTCAMP Organized Globally, Held Locally @GPPBootcampBLR #GPPBootcampBLR #GlobalPowerPlatformBootcamp
  • 2. Mitul Rana Let’s integrate natural language in your Power BI reports and Ask a question on your data. @mitul_rana Platform Specialist, Trelleborg Samik Roy @roy_samik Senior Developer, Born in the Cloud @GPPBootcampBLR #GPPBootcampBLR #GlobalPowerPlatformBootcamp
  • 4. Agenda • Investment areas • Personas and capabilities Overview of AI in Power BI • AI visualizations • Natural language capabilities End User Insights • Automated ML • Cognitive services Analyst toolkit
  • 5. AI in Power BI Sentiment Analysis Key Phrase Extraction Create ML models Explore predictionsPython Integration R IntegrationExtend with Azure ML Integrate into reports AI Visualizations Natural Language
  • 6. AI in Power BI Sentiment Analysis Key Phrase Extraction Create ML models Explore predictionsPython Integration R IntegrationExtend with Azure ML Integrate into reports AI Visualizations Natural Language
  • 8. AI in Power BI Sentiment Analysis Key Phrase Extraction Create ML models Explore predictionsPython Integration R IntegrationExtend with Azure ML Integrate into reports AI Visualizations Natural Language
  • 9. AI in Power BI Sentiment Analysis Key Phrase Extraction Create ML models Explore predictionsPython Integration R IntegrationExtend with Azure ML Integrate into reports AI Visualizations Natural Language
  • 11. Key Takeaways – AutoML POWER BI + AUTOMATED ML ENABLES YOU TO: AUTOMATE MOST OF THE STANDARD MACHINE LEARNING PRACTICES IN A NON-INTRUSIVE YET TRANSPARENT WAY AI IN POWER BI IS NOT ONLY ABOUT ACHIEVING GREAT ACCURACY BUT ABOUT EASY TRANSLATING THIS ACCURACY TO BUSINESS IMPACT
  • 12. Key Takeaways – AutoML Automated ML in Power BI Automated ML in Azure Designed for the business analyst/BI professional Designed for for the data scientist/data engineer Skillset required is Power Query and Power BI dataflows (OOB point and click functions, M code) Skillset required is Azure, Python and Jupyter notebooks ML model lives completely inside Power BI and can only be used by Power BI ML model lives in Azure and can be consumed as a webservice by lots of different applications (including Power BI) Requires Power BI Premium and utilizes capacity Requires an Azure subscription and utilizes Azure consumption
  • 14. Key Takeaways – Cognitive Services Cognitive services in Power BI Cognitive services in Azure Designed for the business analyst/BI professional Designed for for the data scientist/data engineer Skillset required is Power Query and Power BI dataflows (OOB point and click functions, M code) Skillset required is Azure, coding language (C#, Java, Node.js, Python) Lives completely inside Power BI and can only be used by Power BI Live in Azure and can be consumed as a webservice by lots of different applications Require Power BI Premium and utilizes capacity Require an Azure subscription and utilizes Azure consumption
  • 15. Session takeaways Power BI + AI enables: Findings insights seamlessly and interacting with data in a natural way using language Gives analysts the opportunity to enrich their data using AI and build their own machine learning model without writing any code Extension points for data scientists to plug in their Python/R scripts or to bring in machine learning models they created elsewhere
  • 16. Call to Action • Try some of our quick start guides to get started with Automated ML, Cognitive Services and Azure ML on dataflows: • https://docs.microsoft.com/en-us/power- bi/service-machine-learning-automated • Download the latest Power BI Desktop to try AI Visuals • https://powerbi.microsoft.com/en-us/desktop/ • Get in touch with the team to talk to us about your use cases, pain points, roadmap feedback (seriously we love hearing from you ): • Mitul.rana@gmail.com • Samik.n.roy@gmail.com