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QSSUG: Azure Cognitive Services – The Rise of the Machines
- 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 grant@angrykoala.com.au
- 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
- 5. Cortana Intelligence Suite Data sources Apps Sensors and devices People Automated systems Data Intelligence Cortana Intelligence Suite Action Apps
- 6. 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
- 7. Azure Cognitive Services API’s Give your solutions a human side https://www.microsoft.com/cognitive-services/en-us/documentation
- 8. Cognitive API Documentation & Definitions - https://www.microsoft.com/cognitive-services/en-us/documentation Win 10 Intelligent Kiosk App Download – https://www.microsoft.com/en-au/store/p/intelligent-kiosk/9nblggh5qd84 Intelligent Kiosk Code (Github) – https://github.com/Microsoft/Cognitive-Samples-IntelligentKiosk/
- 9. Intelligent Kiosk – Cognitive Services https://www.microsoft.com/en-au/store/p/intelligent-kiosk/9nblggh5qd84
- 10. Azure Cognitive API Regions and Deployment Where can I find them? How can I deploy them? https://ms.portal.azure.com/ What is actually deployed?
- 11. Using Azure Cognitive API’s Accessing the API JSON request JSON response secure base url API version “op”
- 12. Service Tiers and SLA Service Tiers Azure SLA
- 14. Sentiment140” data (A public data set containing millions of tweets)
- 15. Deployment, Tiers and Costs API deployment Service tiers and costs
- 16. Demo - TEXT ANALYTICS API in the Azure Portal
- 18. 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." } ] }
- 19. { "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
- 20. 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
- 21. { "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
- 22. { "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
- 23. 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) https://mrfoxsql.wordpress.com/2016/09/13/azure-cognitive-services-apis-with-sql-server-2016-clr/
- 25. Social Media Pipeline Region: Australia SE Function .Net (C#) Azure SQL DB Sentiment Schema Call Tweet Data Sentiment Key Phrases powerbi.com 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 https://powerbi.microsoft.com/en-us/solution-templates/brand-management-twitter/
- 26. Demo - Leveraging the Text Analytics API with Twitter Social Sentiment https://msit.powerbi.com/view?r=eyJrIjoiNGZiY2NmNDAtNjJmZC00N2U4LTliOGYtNjJmNmNhNjZjZWU5IiwidCI6IjcyZjk4OGJmLTg2ZjEtNDFhZi05MWFiLTJkN2NkMDExZGI0NyIsImMiOjV9
- 27. Key Points / Takeaways – Where to from here?
- 29. Visit Channel 9 to access a wide range of Microsoft training and event recordings https://channel9.msdn.com/ Visit Microsoft Virtual Academy for free online training visit https://www.microsoftvirtualacademy.com Continue your learning path