Slide contents

1. 1. AI Platform Basics

   A basic introduction: what an AI product platform is made of and why demos do not become production by themselves

2. 2. Alexander Polomodov

   Technical Director & Fellow, T-Technologies

   Architecture and engineering R&D

   AI adoption in SDLC

   Focus: platform, not chat

3. 3. 01. Why an AI platform

   Moving from impressive demos to a repeatable way of shipping AI products

4. 4. AI features become production loops

5. 5. AI demos do not become products alone

   Models do not solve operations

   Teams reselect the stack

   Quality failures are not reproducible

   Rights and knowledge drift

   Cost is detached from value

6. 6. 02. AI platform components

   Model layer, context, tools, security, evals, observability and economics

7. 7. What layers make up an AI platform

8. 8. AI platform component map

9. 9. Govern quality, operations and cost

10. 10. Gateway controls the platform

11. 11. Quality starts with context

12. 12. 03. AI product lifecycle

    From idea and spec to eval, release, monitoring and improvement

13. 13. AI product lifecycle

14. 14. The lifecycle continues after release

15. 15. Spec and eval anchor behavior

16. 16. AI features ship as changes

17. 17. 04. Platform operating planes

    Build-time, run-time, control-plane and cost-plane as four separate management tasks

18. 18. The platform works across four planes

19. 19. Platform rails, product accountability

20. 20. Guardrails live in the platform

21. 21. 05. Operating model and maturity

    Who owns the platform, scenarios, data, risk and economics

22. 22. AI platform accountability model

23. 23. How to measure AI platform maturity

24. 24. What we take into AIOps / LLMOps

    Next: operations, agents, evals, economics

    Platform = repeatable AI launch

    Minimum: gateway, context, evals

    Platform provides rails

    Product owns quality and risk

    Next step: manage the AI system as a working product

25. 25. Materials

    For further reading

    OpenAI, Claude, Gemini, Yandex docs.

    LangChain platform concepts.

    Cloud/Data Platform/DataOps lectures.