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.

1. How to measure relationships
within the Company using .NET
Demchuk Alexandr
ademchuk@provectus.com

2. About me
Software Engineer

3. Outline
● The Issue of growth
● Zappos. Bringing happiness
● Implementation Mess
● Rise of the Bots
● ML/ what next?
● Stars and Fails

4. The problem of
Growth
1. Family relations < 50
emp
2. Processes > 150 emp
3. Culture 1000+ emp

5. Culture. Which one?

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. ● 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. Options ?
● Email notifications
● Desktop application
● Interactive application based
on Bots and ML

9. Technology stack

11. Architecture
Rabbit MQ - MassTransit
MS
SQL
MongoDB
MongoDB

12. Deployment
● BitBucket (Code)
● BitBucket pipelines
● ReBuild Docker Containers
● Push to Azure Container
Registry
● Azure. VMs pull new
containers through the Docker-
Compose

13. Rise of the Bots.
The machines rose from the ashes of the
nuclear fire….

14. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)

15. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)

16. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)

17. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)
● Turn, TurnContext, Activity

18. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)
● Turn, TurnContext, Activity
● Dialogs, Prompts

19. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)
● Turn, TurnContext, Activity
● Dialogs, Prompts
● Adaptive Cards

20. Bot Framework basic
Concepts ● ASP Net Core Web API
application (MiddleWare)
● Turn, TurnContext, Activity
● Dialogs, Prompts
● Adaptive Cards
● State management

21. 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..

22. Bot Message

23. Initiate conversation code

24. Bot Answer Clarification

25. Get Answer code

26. Friendship map
First ML Step
● What it is?
● How to build?
● What are the tools?

27. Aha! ML

28. Where to start ?

29. ML.NET
Simplicity ● ML Lifecycle

30. ML.NET
Simplicity ● ML Lifecycle
● Available Scenarios

31. ML.NET
Simplicity ● ML Lifecycle
● Available Scenarios
● Context
● Load Data

32. ML.NET
Simplicity ● ML Lifecycle
● Available Scenarios
● Context
● Load Data
● Pipelines
● Models, Zipping Models

33. ML.NET
Simplicity ● ML Lifecycle
● Available Scenarios
● Context
● Load Data
● Pipelines
● Models, Zipping Models
● Evaluate Model

34. ML.NET
Simplicity ● ML Lifecycle
● Available Scenarios
● Context
● Load Data
● Pipelines
● Models, Zipping Models
● Evaluate Model
● Predict Results

35. Auto ML Magic

36. Scenarios
Algorithms
● Binary Classification
● Multiclass Clustering
● Collaborative Filtering

37. Answer 1 Answer 2 I don’t know
ML Service
Src Dst Src Dst Src Dst
Team 1
Team 2
AmbiguityTeam 3
Team N

38. Some Interesting Analysis

41. 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

42. Q && A