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QSSUG: Azure Cognitive Services – The Rise of the Machines


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Slides from the Queensland SQL Server User Group, May 2017.
Presenter: Grant Paisley, Angry Koala

With advances in computing power, advanced machine learning algorithms and globally consumable data sources the real world use cases for Cognitive Services within our everyday lives has begun to grow significantly.

This fun and demo heavy session first introduces the full suite of Azure Cognitive Services API’s and discusses the approach to solution design, API deployment and consumption within the context of a set of real deployments and practical use cases.

The session then deep dives into 2 of the more commonly deployed Azure Cognitive Services detailing methods available for SQL and .NET developers to code, interact and scale the usage of the Cognitive API services to create interesting, fun and innovative solutions. At the end of this session you will have a much clearer understanding of how the suite of Azure Cognitive Services could be used within your business and the methods available by which you can start to plan, deploy and actively consume these innovative services.

About the Presenter

Grant Paisley is founder of Angry Koala, a small Sydney-based consultancy specialising in Microsoft Data Platform, particularly Power BI and Azure Machine Learning. He’s a Microsoft Data Platform MVP Alumni and passionate about community. You may see him kiting on water or snow, mountain biking or fighting fires with the RFS.

Published in: Data & Analytics
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QSSUG: Azure Cognitive Services – The Rise of the Machines

  1. 1. Based on presentation by Rolf Tesmer - Azure Data Solution Architect, Microsoft Kristina Rumpff - Data Platform Solution Architect, Microsoft Azure Cognitive Services Rise of the machines Grant Paisley
  2. 2. “The Rise of the Machines” – A Primer “Good to see you again” “Would you like to go somewhere warm and have some fun, like the beach?” “…I’d like to book a holiday.” 35yo, male language  english sentiment  unhappy seen before  yes (face, voice) context  directions,store image  beach “…ok, that’s good. Book it!” …review, plan, select… sentiment  happy (face) context  holiday,book “I’m glad I could help make your day better” “Enjoy your holiday next week” Search Sentiment  (face, text) recommendations
  3. 3. Session Agenda
  4. 4. Cortana Intelligence Suite Data sources Apps Sensors and devices People Automated systems Data Intelligence Cortana Intelligence Suite Action Apps
  5. 5. Easily turn data into intelligent action Action People Automated Systems Apps Web Mobile Bots Intelligence Dashboards & Visualizations Personal Digital Assistant Bot Framework Cognitive Services Power BI Information Management Event Hubs Data Catalog Data Factory Machine Learning and Analytics HDInsight (Hadoop and Spark) Stream Analytics Cortana Intelligence Suite Data Lake Analytics Machine Learning Big Data Stores SQL Data Warehouse Data Lake Store Data Sources Apps Sensors and devices Data
  6. 6. Azure Cognitive Services API’s Give your solutions a human side
  7. 7. Cognitive API Documentation & Definitions - Win 10 Intelligent Kiosk App Download – Intelligent Kiosk Code (Github) –
  8. 8. Intelligent Kiosk – Cognitive Services
  9. 9. Azure Cognitive API Regions and Deployment Where can I find them? How can I deploy them? What is actually deployed?
  10. 10. Using Azure Cognitive API’s Accessing the API JSON request JSON response secure base url API version “op”
  11. 11. Service Tiers and SLA Service Tiers Azure SLA
  12. 12. Sentiment140” data (A public data set containing millions of tweets)
  13. 13. Deployment, Tiers and Costs API deployment Service tiers and costs
  14. 14. Demo - TEXT ANALYTICS API in the Azure Portal
  15. 15.
  16. 16. Using the TEXT ANALYTICS API – Part 1 (Sync) Accessing the API JSON request { "documents": [ { "language": "en", "id": "1", "text": "We are having a great time at the conference." }, { "language": "en", "id": "2", "text": "However the weather and the food are bad." } ] }
  17. 17. { "documents": [ { "id": "1", "detectedLanguages": [ { "name": "English", "iso6391Name": "en", "score": "1", } ] }, { "id": "2", "detectedLanguages": [ { "name": "English", "iso6391Name": "en", "score": "1", } ] } ] } { "documents": [ { "id": "1", "keyPhrases": ["great time", "conference"] }, { "id": "2", "keyPhrases": ["weather", "food"] } ] } { "documents": [ { "id": "1", "score": "0.934" }, { "id": "2", "score": "0.176" } ] } TEXT ANALYTICS API response – Part 1 (Sync) JSON response
  18. 18. Why Score Sentiment by Sentence? An Example… Document = “We are having a great time at the conference.” Sentiment Score = 0.95 Key Phrases = [“great time”, “conference”] Document = “However the weather and the food are bad.” Sentiment Score = 0.17 Key Phrases = [“weather”, “food”] Very Positive Very Negative Very Negative Misleading Neutral Sentiment
  19. 19. { "documents": [ { "language": "en", "id": "1", "text": "We are having a great time at the conference." }, { "language": "en", "id": "2", "text": "However the weather and the food are bad." } ], "stopWords": ["bad"], "stopPhrases": ["great time", "weather"], } Using the TEXT ANALYTICS API – Part 2 (Async) Accessing the API JSON request Optional
  20. 20. { "status": "succeeded", "operationProcessingResult": { "topics": [ { "id": "6d327260-9adc-4021-bfec-1cd4d1d08db8", "score": “2", "keyPhrase": “ignite conference" } ], "topicAssignments": [ { "topicid": "6d327260-9adc-4021-bfec-1cd4d1d08db8", "documentid": "1", "distance": "0.176" }, { "topicid": "6d327260-9adc-4021-bfec-1cd4d1d08db8", "documentid": "2", "distance": "0.942" } ], } } TEXT ANALYTICS API response – Part 2 (Async) JSON response
  21. 21. What can you use the Text Analytics API for? Custom Written Application (.net etc) LUIS API Solution Leveraging the BOT Framework Social Media Data Pipeline (Social Sentiment, etc) Embedded within a SQL Server Database (SQL CLR)
  22. 22.
  23. 23. Social Media Pipeline Region: Australia SE Function .Net (C#) Azure SQL DB Sentiment Schema Call Tweet Data Sentiment Key Phrases Azure Machine Learning Data Connection New Power BI Reports (optional) On demand Data Science Power BI Desktop On-Prem Office 365 Power BI Executive Social / Marketing C Level Dashboards Marketing Dashboards Azure Public Cloud Tweets @Handles #Tags Azure Machine Learning Region: Southeast Asia Azure Cognitive Services Region: West US Text Analytic API Sentiment Key Phrases (optional) ML Models Twitter Logic App Check Twitter Every 3 min Social Media Data Pipeline - Architecture
  24. 24. Demo - Leveraging the Text Analytics API with Twitter Social Sentiment
  25. 25. Key Points / Takeaways – Where to from here?
  26. 26. Visit Channel 9 to access a wide range of Microsoft training and event recordings Visit Microsoft Virtual Academy for free online training visit Continue your learning path
  27. 27. References