Week 2 - Python for AI & AI Engineering on AI Platform Engineering Handbook

Python Programming Fundamentals

Mon, 01 Jan 0001 00:00:00 +0000

The Big Picture First

Everything covered throughout the rest of this whole course — RAG systems, agents, fine-tuning pipelines, infrastructure code — is ultimately written in some actual programming language, and for the overwhelming majority of AI and machine learning work today, that language is Python. Before any of the more advanced material makes sense, it’s worth building a genuinely solid foundation in Python itself, since it’s the actual tool you’ll be using to build essentially everything else in this entire course. Python became the dominant language for AI work for a few genuine, practical reasons: it reads almost like plain English, making it genuinely approachable for beginners; it has an enormous ecosystem of pre-built libraries (like the PyTorch and TensorFlow frameworks we mentioned back in the GPU computing explanation) specifically built for AI and data work; and it lets you get something genuinely working quickly, without needing to wrestle with a lot of low-level technical detail that other, more traditional programming languages often require. Let’s build up an understanding of the core fundamentals, piece by piece.

Advanced Python for AI

Mon, 01 Jan 0001 00:00:00 +0000

The Big Picture First

The previous explanation covered the basic building blocks of Python — the vocabulary and grammar you need to write any working code at all. This section covers a set of considerably more powerful features that show up constantly once you start reading and writing genuinely real-world AI code, rather than small, simple beginner exercises. Here’s why these particular topics matter so much specifically for AI engineering: the frameworks we’ve discussed throughout this entire course — the agent frameworks from Week 8, the vector databases, the API clients — are almost all built using exactly these particular advanced features. Understanding them well is really the difference between being able to write your own simple scripts, and being able to genuinely read, understand, and properly work with the real, production-grade AI code and AI libraries that this entire course has been discussing throughout its whole duration.

Python for Data & ML

Mon, 01 Jan 0001 00:00:00 +0000

The Big Picture First

The previous two explanations covered Python as a general-purpose programming language — the syntax, the control flow, the more advanced features that show up throughout real-world AI code. This final Week 2 explanation covers a specific, specialized set of tools built on top of that same general foundation, specifically designed for actually working with data at scale, and for actually building traditional machine learning models. It’s worth understanding why this particular material matters even in a course that’s spent so much of its time on large language models specifically — LLMs don’t exist in isolation from the broader field of data science and machine learning; they’re built using many of these exact same underlying tools, and a huge amount of real-world AI engineering work (cleaning a dataset before fine-tuning, analyzing evaluation results, building the traditional ML components that often sit alongside an LLM in a real production system) genuinely relies on exactly this material. Let’s build it up piece by piece.

Python for GenAI Applications

Mon, 01 Jan 0001 00:00:00 +0000

The Big Picture First

This final Week 2 explanation is really where everything from the previous three Python explanations comes together into something genuinely practical and directly relevant to the rest of this entire course. Everything up to this point — the syntax, the data structures, the OOP and async concepts, the NumPy and Pandas fundamentals — has been building toward this: actually writing the real, working Python code that powers a genuine, working GenAI application. This is the layer where Python stops being an abstract set of skills, and starts being the actual glue that connects together everything we’ve discussed throughout the entire rest of this course — calling LLM APIs, building web servers to expose your own AI features, working with vector databases, and properly testing that it all genuinely works. Let’s build it up.