You know Machine Learning, your models are working well, the team likes the results… but now you need to “serve” them in an API so that others can interact with it (developers/frontend team/other systems). In this talk, you will learn how to easily build a production-ready web (JSON) API for your ML models with FastAPI, including best practices by default... explained with memes. With very little code, you will get automatic/interactive documentation, data validation, authentication, open standards (OpenAPI, JSON Schema, OAuth2), and the best performance available in Python (on par with Go and NodeJS). On top of that, you will have autocompletion and type checks in your editor, even for your own data, no matter the complexity of its shape.

1. Serving ML easily with
High performance, easy to learn,
fast to code, ready for production

2. Who am I?
Sebastián Ramírez
github.com/tiangolo
linkedin.com/in/tiangolo
twitter.com/tiangolo
Dev at Explosion
Berlin, Germany
Explosion created:
I created:
tiangolo.com

3. About FastAPI
● 16K GitHub stars (about 1K+ per month)
● Used by Microsoft, Uber, Netflix, etc.
● Performance in the top rank for Python
@tiangolo
But why would it be useful for you,
Machine Learning people?

4. FastAPI for ML people
I will assume you know:
● Machine Learning
● The basics of web / API development
● HTTP, JSON...
@tiangolo

5. Based on standards
● OpenAPI
● JSON Schema
● OAuth2
● Automatic API docs
@tiangolo

6. Based on standards - for free
@tiangolo

7. Based on Python type hints
● Autocompletion everywhere
● Type checks and type errors
@tiangolo

8. Types provide autocompletion
@tiangolo

9. Easy and short
@tiangolo

10. Less API code
@tiangolo

11. You have an ML model
@tiangolo

12. You have an ML model
@tiangolo

13. Make it a web API
@tiangolo
● Show your colleagues
● Integrate with other internal apps
● Deploy to production

14. Basic FastAPI app
@tiangolo
https://somedomain.com/items/5?q=some+query

15. Basic FastAPI app - docs
@tiangolo
https://somedomain.com/docs

16. Automatic docs
@tiangolo

17. Basic FastAPI app with body
@tiangolo

18. Basic FastAPI app with body - docs
@tiangolo

19. Basic FastAPI app with body - docs
@tiangolo

20. Basic FastAPI app with body
@tiangolo

21. Basic FastAPI app with body and query
@tiangolo

22. Basic FastAPI app with body and query - docs
@tiangolo
https://somedomain.com/article/?big_model=true

23. Basic FastAPI app with body and query
@tiangolo

24. FastAPI app with ML
@tiangolo

25. FastAPI app with ML - docs
@tiangolo

26. JSON array of objects in body
@tiangolo

27. JSON array of objects in body - docs
@tiangolo

28. JSON array of objects in body - invalid data
@tiangolo

29. JSON array of objects in body - validation
@tiangolo

30. JSON array of objects in body - validation
@tiangolo

31. FastAPI auto-completion
@tiangolo

32. Type checks
@tiangolo

33. All based on standard Python types
@tiangolo

34. Metadata
@tiangolo

35. Metadata in docs
@tiangolo

36. Metadata in docs
@tiangolo

37. Required query
@tiangolo

38. Required query in docs
@tiangolo

39. Required query validation
@tiangolo

40. Required query in docs - valid
@tiangolo
https://somedomain.com/article/?lang=en

41. Required query in docs - response
@tiangolo

42. Required query in docs
@tiangolo

43. Validation
@tiangolo

44. Validation in docs - invalid
@tiangolo

45. Validation in docs - valid
@tiangolo

46. Data validation
@tiangolo

47. FastAPI - Performance
@tiangolo

48. FastAPI - Performance
@tiangolo

49. FastAPI - Other features
@tiangolo
● Dependency Injection
● Security - OAuth2
● WebSockets
● Files
● Background Tasks
● Easy GraphQL integration
● More...

50. Other tools
@tiangolo
Build great CLIs. Easy to code.
Based on Python type hints.
Functional deep learning with types,
compatible with your favorite libraries.
typer.tiangolo.com thinc.ai

51. Thank you!
Sebastián Ramírez github.com/tiangolo
linkedin.com/in/tiangolo
twitter.com/tiangolo
tiangolo.com
Questions?
fastapi.tiangolo.com