Model Online / On-Site
Duration
Modules 5 modules

Course Objective & Scope

Turning experimental notebook scripts into maintainable, high-performance AI libraries and data pipelines requires deep mastery of Python's internals. This course covers Python's object model, memory management, concurrency primitives and modern software engineering tooling through practical AI and data engineering workloads.

Participants process large data streams in constant memory with generators, overcome I/O and CPU bottlenecks using asyncio and multiprocessing, pinpoint slow code with cProfile and memory_profiler, and package internal libraries with pyproject.toml and uv. The programme finishes by refactoring a raw ML prototype into a tested, installable Python package.

Who Should Attend?

  • Data scientists and ML engineers refactoring notebook code into production modules
  • Python developers building high-throughput data processing and LLM orchestration services
  • Platform engineers authoring shared internal Python SDKs and libraries
  • Prerequisite: solid grasp of core Python (functions, classes and standard data structures)

Key Takeaways

  • Design robust interfaces with dataclasses, Pydantic, Protocol and static type checking (mypy/pyright)
  • Process large datasets with minimal RAM using iterators, generators and context managers
  • Navigate the GIL and choose properly between asyncio, ThreadPoolExecutor and ProcessPoolExecutor
  • Profile CPU and memory bottlenecks and accelerate hot loops with NumPy vectorisation or Numba
  • Build, lint, test and distribute internal Python packages using pyproject.toml, uv, ruff and pytest

Curriculum

Advanced Object Model, Type System and Functional Constructs
  • Dunder methods, __slots__, dataclasses, typing.Protocol and Generics
  • Parameterized decorators, closures, functools and custom context managers (contextlib)
Memory Management, Iterators and Generator Pipelines
  • Reference counting, garbage collection, weakref and common memory leak patterns
  • Lazy data-loading pipelines with generator expressions, yield and itertools
Concurrency and Parallelism: asyncio, Threading and Multiprocessing
  • The GIL (Global Interpreter Lock), free-threaded Python and I/O-bound vs CPU-bound workloads
  • High-concurrency API and LLM calls with asyncio (TaskGroups, Semaphores)
  • Multi-core data processing with concurrent.futures, multiprocessing and shared_memory
Profiling and High-Performance Python
  • Bottleneck analysis with cProfile, py-spy and memray / memory_profiler
  • NumPy memory layout (views vs copies), vectorisation and accelerating loops with Numba JIT
Modern Packaging, Code Quality and Testing
  • Standard project layouts with pyproject.toml, uv / Poetry, ruff and mypy
  • Testing ML and data pipelines using pytest fixtures, parametrisation and mocks

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