Python

The Python topic organizes practical Python experience from script automation to production AI systems. It covers daily automation, engineering tools, backend APIs, data processing and data engineering, and also focuses on machine learning training and evaluation, large model application development, RAG/Agent engineering, FastAPI/asynchronous services, C/C++/CUDA bindings, performance profiling, and production deployment.

Core Concerns

• Automation and engineering tools: Daily task automation, file processing, batch scripts, system administration scripts, CLI tools, and developer toolchains
• Backend services and APIs: FastAPI, Django, Flask, REST/HTTP APIs, asynchronous I/O, task queues, service boundaries, and interface design
• Data processing and data engineering: Pandas, NumPy, data cleaning, data transformation, batch pipelines, data quality checks, and reproducible analysis
• Machine learning practice: Feature engineering, model training, model evaluation, experiment management, inference services, model performance, and data leakage control
• Large model application development: Prompt engineering, RAG, agents, vector search, context management, model API wrappers, LLM application evaluation, and security boundaries
• AI engineering and deployment: FastAPI inference APIs, Dockerized deployment, configuration management, logging and monitoring, cost control, stability governance, and progressive delivery
• Performance optimization and internals: Python memory model, GC, GIL, concurrency and multiprocessing, Cython/pybind11/C extensions, profiling, and bottleneck diagnosis

Applicable Scenarios

It applies to the full path from personal scripts, developer tools, data analysis notebooks, and Web APIs to machine learning pipelines and large model application services. For engineers, Python is both a glue language for fast validation and an engineering platform that connects data, models, systems, and production environments. For AI/ML scenarios, the focus is not only calling a model, but also reasoning about data processing, model inference, service stability, performance cost, and engineering governance as one system.