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.NET Fest 2019. Александр Демчук. How to measure relationships within the Company using .Net

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The majority of companies are struggling with an issue of growth where they started from handful group of people and reached 1000+ employees. In case of small groups the relationships remind family’s connections so everyone knows each other personally and the atmosphere is warm and charm whereas starting from 500+ to 1k keep it as family is tough and most of the companies come up with processes on one hand and culture on other. There ain’t no silver bullet how to manage it and in the end of the day they are balancing working a lot about good atmosphere in company and setting up relationships between teams. The issue isn’t new however tools and approaches evolved a lot this is where .NET Core and ML.Net comes in handy. These days AI/ML take over the world and bots as one of implementations are already a part of our life therefore our entuthiastic team created a bot using latest .NET Bot Framework v4 who helps managers to “measure” atmosphere in teams and crucial to socialize teams leveraging the power of ML.NET.

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.NET Fest 2019. Александр Демчук. How to measure relationships within the Company using .Net

  1. 1. How to measure relationships within the Company using .NET Demchuk Alexandr ademchuk@provectus.com
  2. 2. About me Software Engineer
  3. 3. Outline ● The Issue of growth ● Zappos. Bringing happiness ● Implementation Mess ● Rise of the Bots ● ML/ what next? ● Stars and Fails
  4. 4. The problem of Growth 1. Family relations < 50 emp 2. Processes > 150 emp 3. Culture 1000+ emp
  5. 5. Culture. Which one?
  6. 6. “Our Business Strategy is to invest in company culture, with the belief that the culture will ultimately drive employee productivity, customer service quality and brand Strength” – Tony Hsieh
  7. 7. ● Assumptions, norms, and concerns shared by people ● How things get done, without people having to think about it ● The character of an organization We’ve been inspired by Zappos company? Culture =
  8. 8. Options ? ● Email notifications ● Desktop application ● Interactive application based on Bots and ML
  9. 9. Technology stack
  10. 10. Architecture Rabbit MQ - MassTransit MS SQL MongoDB MongoDB
  11. 11. Deployment ● BitBucket (Code) ● BitBucket pipelines ● ReBuild Docker Containers ● Push to Azure Container Registry ● Azure. VMs pull new containers through the Docker- Compose
  12. 12. Rise of the Bots. The machines rose from the ashes of the nuclear fire….
  13. 13. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare)
  14. 14. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare)
  15. 15. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare)
  16. 16. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare) ● Turn, TurnContext, Activity
  17. 17. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare) ● Turn, TurnContext, Activity ● Dialogs, Prompts
  18. 18. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare) ● Turn, TurnContext, Activity ● Dialogs, Prompts ● Adaptive Cards
  19. 19. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare) ● Turn, TurnContext, Activity ● Dialogs, Prompts ● Adaptive Cards ● State management
  20. 20. Bot Framework basic Concepts ● ASP Net Core Web API application (MiddleWare) ● Turn, TurnContext, Activity ● Dialogs, Prompts ● Adaptive Cards ● State management ● SDK v3 vs SDK v4 • Introduction of a bot adapter. The adapter is part of the activity processing stack. • Refactored state management. • A new Dialogs library. • Support for ASP.NET Core..
  21. 21. Bot Message
  22. 22. Initiate conversation code
  23. 23. Bot Answer Clarification
  24. 24. Get Answer code
  25. 25. Friendship map First ML Step ● What it is? ● How to build? ● What are the tools?
  26. 26. Aha! ML
  27. 27. Where to start ?
  28. 28. ML.NET Simplicity ● ML Lifecycle
  29. 29. ML.NET Simplicity ● ML Lifecycle ● Available Scenarios
  30. 30. ML.NET Simplicity ● ML Lifecycle ● Available Scenarios ● Context ● Load Data
  31. 31. ML.NET Simplicity ● ML Lifecycle ● Available Scenarios ● Context ● Load Data ● Pipelines ● Models, Zipping Models
  32. 32. ML.NET Simplicity ● ML Lifecycle ● Available Scenarios ● Context ● Load Data ● Pipelines ● Models, Zipping Models ● Evaluate Model
  33. 33. ML.NET Simplicity ● ML Lifecycle ● Available Scenarios ● Context ● Load Data ● Pipelines ● Models, Zipping Models ● Evaluate Model ● Predict Results
  34. 34. Auto ML Magic
  35. 35. Scenarios Algorithms ● Binary Classification ● Multiclass Clustering ● Collaborative Filtering
  36. 36. Answer 1 Answer 2 I don’t know ML Service Src Dst Src Dst Src Dst Team 1 Team 2 AmbiguityTeam 3 Team N
  37. 37. Some Interesting Analysis
  38. 38. Stars and Fails What went wrong and not ● Teammates resistance ● Bot admin rights ● WebJobs vs Containers ● Net Core docker images. Kubernetes ● DevOps ? ML ? ● ML Immense
  39. 39. Q && A

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