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Python for AI/ML Production

Name: Python for AI/ML Production
Availability: InStock

with LaunchFar AI

10 weeks suggested235 lessonsabout 38 hours total

Go from print() to shipping the three services an AI product runs on: a trace pipeline, an async model gateway, and an eval runner.

For: Developers and data practitioners moving into AI engineering.
You need: No Python needed to start. Nothing to install. Every challenge runs in your browser.

Production Python for AI and ML engineering: 235 hands-on lessons across 67 modules, every one of them running in your browser. Starts at the runtime and the core data structures, then turns to the craft an AI system needs once it has real users: failure policy, bounded work, cost metering, streaming, and logs you can query. Ends on three staged capstone builds. Built for developers and data practitioners moving into AI engineering. Personalized to your skill level so you only see the modules you actually need.

Syllabus

10 tracks · 67 modules · 235 lessons

Python Fundamentals

Pull the fields you need out of a raw log line, and write a function that survives a bad record instead of crashing on it.

10 modules · 36 lessons

1. Runtime and first steps
- Meet the runtime6 min
- First signals to stdout4 min
- Comments and multi-line output7 min
2. Variables and types
- Naming the pipeline's knobs5 min
- Rebinding config mid-run6 min
- The core types in a trace record5 min
- type() checks before the cast6 min

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Data Structures & Iteration

Roll a batch of nested trace records up into a per-model summary, rank it, redact the PII, and write it back out as JSON or CSV.

11 modules · 39 lessons

11. Lists

Building the routing service's allow-list4 min
Picking a model out of the routing list4 min
Adding a new model to the routing list with `.append()`5 min
in-checks and len on the batch5 min

12. Dictionaries

Looking up a per-tenant config value by key4 min
Adding a new model entry to the per-tenant config4 min
Iterating a per-model price dict for the cost report4 min

13. Tuples, sets and None

Reading an immutable allow-list snapshot tuple4 min
Deduplicating a model-name list with a set5 min
Marking a not-yet-rated trace with `None`3 min
Guarding the cost aggregator against not-yet-set scores4 min

14. Comprehensions

Building a list of squared latencies in one expression4 min
Filtering high-latency calls with a comprehension `if`4 min
Squaring only the even values in one pass4 min
Inverting the model-price table7 min

15. Nested data

Walking a list of trace dicts to extract names4 min
Walking a per-tenant config's list of allowed models4 min
Rollup aggregations5 min

16. Iterators and generators

iter/next on the trace stream6 min
Lazy pipelines in one line6 min
yield-ing tokens one at a time7 min
Chained generators as a streaming ETL8 min

17. collections and itertools

Counter for token frequency tables6 min
Grouping traces by model6 min
The sliding context window6 min
islice, chain, groupby on batches8 min
Batches from a stream you cannot measure11 min

18. Sorting and searching

Ranking retrieval hits by score7 min
Binary search and top-k without sorting twice7 min
The page that shows the same row twice12 min

19. Regular expressions

Finding the model name in the log line7 min
Extracting structured fields from raw text7 min
Redacting PII from prompts7 min

20. Dates and times

Trace timestamps without tears6 min
Latency windows with timedelta6 min

21. Serialisation and formats

Parsing the tool-call payload6 min
Config files on disk6 min
The cost report as CSV7 min
Model artifacts and why pickle bites7 min

Objects, Functions & Design

Model a trace record as a dataclass, and swap one provider client for another without touching the code that calls it.

10 modules · 36 lessons

22. Classes and instances

A Book class with title + author attributes5 min
Adding a `describe` method to a Rectangle class5 min
Two ModelConfig instances, two different model targets4 min
Build a class that owns a rendering step8 min

23. First-class functions

A table of functions, not a chain of branches9 min
A counter that survives the return11 min
The rule travels as an argument9 min

24. Decorators

Wrapping a call without touching the function11 min
Three layers, because each one receives something different14 min
The wrapper must not eat the name10 min
Three decorators, two orders14 min

25. Type hints

Annotations that carry the contract8 min
Values that can be missing, containers that say what they hold9 min
Type the shape you call, not the class you were handed10 min
Running the checker the interpreter never runs12 min

26. Inheritance and polymorphism

One base client, one provider subclass8 min
Extending a base method instead of copying it8 min
One dispatch loop, three unrelated clients8 min
A Retriever interface that refuses to be half-implemented10 min

27. Magic methods and protocols

Trace records that debug themselves8 min
Comparing and sorting eval runs10 min
A Dataset that answers len and indexing8 min
A span that closes even when the block raises10 min

28. Dataclasses and properties

The record type you stop hand-writing8 min
Defaults that are not shared8 min
Computed attributes that cannot go stale9 min
Rejecting a bad record at construction8 min

29. Enums and typed structs

A closed set of tiers instead of loose strings8 min
Immutable records that are still tuples8 min
Typing a payload the provider might not send9 min

30. Design patterns

Swapping the chunker without touching the splitter10 min
One place that knows how to build a client10 min
One reply type, two provider payloads10 min
A tool catalog the agent can grow12 min

31. SOLID in practice

The five principles, on a pipeline you can read12 min
Split the reporter, hand it a client14 min

Building your catalog.

Python for AI/ML Production

Python Fundamentals

1. Runtime and first steps

2. Variables and types

Start this course

3. Numbers and math

4. Strings and text

5. Conditions and branching

6. Loops and iteration

7. Functions and scope

8. Error handling

9. Input, output and files

10. Modules and imports

Data Structures & Iteration

11. Lists

12. Dictionaries

13. Tuples, sets and None

14. Comprehensions

15. Nested data

16. Iterators and generators

17. collections and itertools

18. Sorting and searching

19. Regular expressions

20. Dates and times

21. Serialisation and formats

Objects, Functions & Design

22. Classes and instances

23. First-class functions

24. Decorators

25. Type hints

26. Inheritance and polymorphism

27. Magic methods and protocols

28. Dataclasses and properties

29. Enums and typed structs

30. Design patterns

31. SOLID in practice

Functional Python & Metaprogramming

32. Functional toolkit

33. Class creation and registries

34. Introspection

35. Code generation and DSLs

Concurrency & Async

36. Concurrency concepts

37. asyncio foundations

38. Advanced asyncio

39. Executors and parallelism

40. Distributed work and resilience

41. Bridging sync and async

Testing, Tooling & DevOps

42. Testing fundamentals

43. Debugging

44. Quality and docs

45. Packaging and environments

46. CI/CD

47. Security essentials

48. Code review

Performance & Internals

49. Profiling

50. Memory

51. Object model and bytecode

52. AST: code as data

53. Native code and runtimes

Applied Python

54. Databases and SQL

55. HTTP and APIs

56. CLI tools

57. Web frameworks orientation

58. Data stack orientation

Production Patterns

59. Failure policy

60. State machines

61. Bounded work

62. Metering

63. Streaming

64. Observability

Capstones

65. Capstone: Trace Analytics Pipeline

66. Capstone: Async Model Gateway

67. Capstone: Eval Runner CLI