Creating any type of company takes enormous amounts of effort, hard work, and persistence. Let alone an Artificial Intelligence company. As we can assure you, it will take a lot more than the above and adding just a team of brilliant AI scientists to build complex real-world AI solutions. In this talk, we will show you the crucial roles of development teams in a high-performing Artificial Intelligence company.

1. Behind the scenes development
in an AI company
Matija Ilijaš

6. Behind the scenes development
• Development that is not necessarily visible to the outside
• Can drastically vary in size between seemingly similar companies
• Requires more resources and more expertise to scale
• Gives more room for engineers to push the limits of a complex system

8. Real-time data extraction
powered by AI

12. +100 million end users

18. AI models

19. Data
Model
Evaluation
Data
Management
Model
Training
Model
Deployment

20. Data
Model
Evaluation
Data
Management
Model
Training
Model
Deployment

21. Garbage in, garbage out
DATA ANNOTATION ACCURACY
RANDOM 50%
CROWSOURCED 95%
THIRD PARTY SERVICE 98%
PROPRIETARY SYSTEM 99,9%

22. Data management system
• Used by our team of domain experts and data annotators
• Enables maximum annotation efficiency with its custom UI
• Runs several quality assurance mechanisms to ensure data quality
• Performs preannotation with our currently best AI models
• Custom built annotation workflow for each product

23. Development
• Software and UI developers
• DevOps, Data and QA engineers
• Internal clients: domain experts and data annotators
• .NET Core microservices using Docker and Kubernetes
• MongoDB, SQL Server on Linux and ElasticSearch
• Figma, ReactJS and enterprise-class UI library

24. Data
Model
Evaluation
Data
Management
Model
Training
Model
Deployment

25. Production-Scale Machine Learning

26. Machine learning engine
• Enables our AI development process
• Optimizes the data management process with AI model serving
• Repository of proprietary neural network layers, architectures and training
procedures
• Production-scale model training and evaluation with deployment

27. Development
• Software and DevOps engineers
• Experienced with software architecture and microservice development
• Python with emphasis on internal Tensorflow integration
• Microservices using Docker and Kubernetes
• Pipeline definition and execution with Argo
• Metadata storage with MongoDB and PostreSQL

28. Running the models

29. AI inference engine

30. AI inference engine
• Optimizing our needs while open source engines are optimizing
everyone’s needs
• Implemented in C++, completely cross-platform
• Maximum control over privacy of the models
• Multi-model support optimized in both speed and memory footprint
• Our playground for innovation and new products

31. Development
• C++ developers
• Highly optimized neural network inference
• Latest C++ standards
• Conan C/C++ Package Manager
• Build system with Cmake and Jenkins
• WebAssembly

32. Putting it all together

33. Complex AI system
• It’s design determines which AI models are needed and how they will
interact to solve the problem
• Additional processing units are implemented by product developers
• Allows control over the parts of the system where crucial or sensitive
decisions are made
• Implemented and exported as a static C++ library

34. Development
• C++ developers
• Experience in Computer vision
• Product development with AI models
• Compile-time and meta programming
• Latest C++ standard packaged with Conan

35. Deployment on all platforms

36. Mobile deployment
• Custom Camera management
• SDKs developed for iOS and Android
• Cross-platform auto generated using proprietary code generation tools
• Emphasis on simple UX and use of platform specific features
• Always exploring new features on platforms

37. Web deployment
• Custom Web API
• AI system static C++ library wrapped in a Docker container
• Software as a Service (SaaS)
• In-Browser scanning with our AI inference engine powered by
WebAssembly