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Machine Learning at Hand with Power BI

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Machine Learning is approaching a peak of inflated expectations, although we see AI daily and in all contexts. Media pressure is high, governments are overly optimistic, plenty of ventures are putting money in unviable ideas or some brilliant engineers fail to reach business users.
But Microsoft bring all of this under the same roof and unleash the power of AI by integrating Power BI ecosystem with Azure ML and Cognitive services. The result is as simple and effective as great technology at end-user's hand.
This session is not about learning how to do AI but how to make AI usable and add value. Integrating ML models and sophisticated cognitive services in reports, understanding concealed relations and bringing automated ML empowers any business user to exploit AI for better decisions, regardless of his technical skills.

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Machine Learning at Hand with Power BI

  1. 1. April 27 GLOBAL AZURE BOOTCAMP IS POWERED BY: Machine Learning at Hand with Power BI How Developers and Data Scientists could Bring AI to the Business User
  2. 2. Thanks to our Sponsors: Global Sponsor: Platinum Sponsors: Gold Sponsors: Silver Sponsors: Swag Sponsor: General Sponsor:
  3. 3. About me • Software Architect @ o 17+ years professional experience • Microsoft Azure MVP • External Expert Horizon 2020 • External Expert Eurostars-Eureka, InnoFund Denmark • Business Interests o Web Development, SOA, Integration o IoT, Machine Learning, Computer Intelligence o Security & Performance Optimization • Contact ivelin.andreev@icb.bg www.linkedin.com/in/ivelin www.slideshare.net/ivoandreev
  4. 4. AGENDA The ML Hype The ML Features in PBI PBI Premium, Pro, Embedded Auto ML Quick Insights Text Analytics Key Influencers Custom R/Python Demo
  5. 5. “Difference between ML and AI? - If it is written in Python, probably it is ML. - If it is written in PowerPoint, probably it is AI.” * * * “When you’re fundraising, it’s AI. When you’re hiring, it’s ML. When you’re implementing, it’s linear regression.“ * * * “How many data scientists it takes to change a light bulb? (A data scientist at heart would say “None, as it is a HW problem”) In fact it is 20 seniors and 1 intern. Data scientists will argue over a month on the right approach, the intern will copy the solution from StackOverflow.” Wondering why AI does not Provide the Expected
  6. 6. Gartner’s Hype Cycle 3. Trough of disillusionment 4. Slope of enlightment 5. Plateau of productivity Expectations Time 1. Technology Launch generates significant interest 2. A peak of inflated expectations
  7. 7. “Business impact from AI initiatives takes much longer than anticipated” * * * “Classical ML techniques are extremely underrated.” * * * “…many organizations are pushing to apply deep learning techniques without even understanding how they apply to their current initiatives.” * * * “Through 2022, over 75% of organizations will use DNNs for use cases that could be addressed using classical ML techniques” Chirag Dekate, Sr. Research Director
  8. 8. Hype Cycle for Midsize Enterprises, 2018
  9. 9. Users are Drowning in Data • Environment generates massive data volumes • Understanding requires AI for exploration • Users are asking to get insights with no code The Microsoft Approach • Place AI in the hands of the end-users • Expose backend data science work
  10. 10. Microsoft Place AI in the Hands of End-Users • Azure Cognitive Services o ML algorithms to extract information from unstructured sources o Vision, speech and facial recognition, language understanding • Key Driver Analysis o Help users understand what combination of impact features determines a KPI o Automatically point most important factors • Integrate ML models in PBI o Azure ML models shared by data scientists to business analysts • Own ML Models in PBI o Business Analysts produce own ML models without writing code o Use Automated ML features targeting users, rather than developers
  11. 11. PBI Target Audience, Licensing and Pricing • PBI Portfolio o On-Premises: Desktop, Mobile, Report Server o Service: Free, Pro, Premium(EM/P SKU), Embedded (A SKU) • New AI features N/A in Pro o AI Preview requires A2 SKU but provisioning fails on A2 SKU o Hint: Create on A4, downscale to A2 • “A” SKUs identical to “EM/P” o Pro – 8.8 EUR User/ М (Shared) o A1 SKU – 630 EUR / M (equals EM1) o A2 SKU – 1’256 EUR / M (equals EM2) o A3 SKU – 2’518 EUR / M (equals EM3) o A4 SKU – 5’041 EUR / M (equals P1) o A5 SKU – 10’087 EUR / M (equals P2) o A6 SKU – 20’180 EUR / M (equals P3) PBI User Licensing PBI Free PBI Pro Capacity Licensing P SKU EM SKU A SKU A SKU EM SKU P SKU Purchase Azure O365 O365 Sharing Use Case Embedded rep. Embedded rep. SharePoint MS Teams Embedded rep. SharePoint MS Teams PBI Apps Billing Hourly Monthly Monthly Commitment No Year/Month Year/Month
  12. 12. PBI Service - Pro vs Premium PRO PREMIUM Highlights License individual users • Create content • Consume content License capacity to serve multiple users: • No additional cost to view content • Creators still need PBI Pro Size Small-medium deployments (i.e. 200 users) Cost efficient from 500 viewers Cost Calculator: https://powerbi.microsoft.com/en-us/calculator/ Hardware • Shared capacity=shared resources • Limitations ensure QoS (1GB file size) • Dedicated hardware, consistent performance • Larger data volumes (10GB file size) Functionality • Dataset refresh - 8 times / 24h • Dataset refresh – 48 times / 24h • PBI Report Server • Data flows • AI workloads • Embedded deployment • Sharing, Personalization, Geolocation
  13. 13. Power BI Pro AI Features • Key Influencers • Quick Insights • Cognitive services (manual) • Python / R script Power BI Premium AI Features • AI insights & Cognitive Services • Automated ML
  14. 14. Automated ML in PBI (Preview) • Enables Business Analyst to create and train model directly in PBI • Requires Premium Workspace • Supports Binary Prediction, Classification and Regression models • Auto ML Service automatically o Extracts meaningful parameters from query o Splits data in training and validation dataset o Performs training with multiple models o Summarizes accuracy of model o Apply model to future data for predictive insights o AI insights allow direct access to Cognitive services https://sqlbits.com/Sessions/Event18/Power_BI_Premium_on_a_budget https://docs.microsoft.com/en-us/power-bi/service-machine-learning-automated
  15. 15. Model Performance Overview
  16. 16. Accuracy Report
  17. 17. Training Details
  18. 18. PBI Desktop Quick Insights Use insights in Power BI Desktop to explain increases and decreases seen in visuals Text Analytics Use Azure Cognitive Services text analytics APIs to enrich dataset in PBI M Query
  19. 19. Quick Insights What is “Quick Insights”: • Sophisticated ML against dataset and output to formatted visuals • Output visuals can be integrated in reports How it works: o PBI Service: Datasets > … > “Generate Quick Insights” o PBI Desktop: Data point > right-click > “Analyze” Key Limitations: o Not executed against DirectQuery, Streaming and Live connect o Non-numeric measures are not supported o Power BI Desktop is limited to the local dataset
  20. 20. Text Analytics in Azure with Power BI • What is “Text Analytics”: o Identify meaning and topics from unstructured data • Limitations: o Up to 17 languages supported (Apr 2019) • How it works: o Register Azure Cognitive Services Text API Key o M-language query from new Power Query
  21. 21. Text Analytics (Step by Step) 1. Create new query in left queries panel 2. Open View > “Advanced Editor” 3. Write the M-code function query 4. On target query, add function column 5. Set column, func. query and parameter 6. Use column in report (i.e. WordCloud) ➀➁ ➂ ➃ ➄ 
  22. 22. Key Influencers Visual Explain the factors that drive a metric of interest Top Segments Explain what combination of factors are most influential
  23. 23. Key Influencers (preview) One of the key ML usages is to find hidden insights What is “Key Influencers”: • Understand factors that impact a metric of interest • Understand the importance of each factor • Top segment factor combinations Limitations o Works for categorical & numeric fields o Not supported for measures and aggregates o Not supported in PBI Embedded and Mobile Note: Increasing the number of categories to analyze means there are fewer observations per category.
  24. 24. The Key Influencers Visual 1. Tabs o Top contributors / Top segments 2. Metric of interest 3. Restatements o Help to interpret the pane visuals 4. Average line o Determined by factors in black 5. Checkbox o Show only influencers ➀ ➁ ➂ ➂ ➃ ➄
  25. 25. The Top Segments Visual 1. Impact of combination of factors on metric 2. Metric of interest 3. Order of segments o Ranked by % of metric of interest o Higher proportion ⇨ higher bubble 4. Segment distribution details o Number of data points ⇨ size of segments 5. Drill down o Segment details and deep dive ➁ ➃ ➀ ➂ ➄ ➄
  26. 26. How does Key Influencers Work Key Influencers • Runs on ML.NET framework • Logistic regression to search patterns • For target category o Split data points “Category” vs “Not the Category” o Determine features that distinguish two classes • Features with little data points are not considered factors Top Segments • Runs on ML.NET framework • Decision trees to find subgroups • For each factor o Decide on best split o Check representative set reached • Filters are grouped into segments
  27. 27. Custom AI with Python/R PBI models with custom and R/Python visuals
  28. 28. Custom AI with Python/R • PBI Visuals & Power Query with Python since Feb 2019 (preview Aug 2018) • PBI Desktop automatically detects runtimes o Install: https://www.python.org/downloads/, https://www.rstudio.com/ o Packages: pip install [name]; install.packages(‘[name]', dependencies = TRUE) • Very good for interactive reports trained from past data • Key Limitations o 150’000 rows, 250MB, 2MB image, 1 GB RAM o 60s (Service) / 300s (Desktop) o PBI Service: limited packages; Python 2.7 and 3.7 • Python IDEs (write and debug Python script) o Jupyter NB? ML Workbench? VS2019 Python Support
  29. 29. PBI Prediction with Custom Model https://towardsdatascience.com/how-to-predict-values-from-a-custom-r-model-in-power-bi-3364f83b0015 * Publish to web is currently not supported for R visuals (April 2019)
  30. 30. Takeaways & References • About Automated Machine Learning o https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-automated-ml • Power BI Guided Learning o https://docs.microsoft.com/en-us/power-bi/guided-learning/ • Azure Data-AI-IoT (Samples, Training & Tutorials) o https://github.com/Azure/data-ai-iot • PBI for Data Science o https://www.pbiusergroup.com/communities/community- home?CommunityKey=5b43099c-d801-49ed-af87-f1da8311f41e • PBI Samples o https://docs.microsoft.com/en-us/power-bi/sample-datasets • Starter Books
  31. 31. Microsoft Power BI ServiceMicrosoft Power BI Desktop
  32. 32. Thanks to our Sponsors: Global Sponsor: Platinum Sponsors: Gold Sponsors: Silver Sponsors: Swag Sponsor: General Sponsor:

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