Python Foundations for MLOps

Before we automate model training or orchestrate pipelines, we need rock‑solid command of core Python. In this kick‑off session we’ll revisit the language fundamentals that every MLOps engineer leans on daily.

#### What we’ll cover in the 2‑hour open lesson

| Topic | Quick take‑away |
| ----- | --------------- |
| Variables & naming conventions | Write code that’s readable in shared repos. |
| Built‑in data types (ints, floats, strings, lists, dicts, sets, tuples) | Choose the right structure for memory‑efficient data handling. |
| Operators (arithmetic, comparison, logical, membership) | Craft concise data‑processing expressions. |
| Control flow |

• `if/elif/else`

• `for` & `while` loops | Make data‑dependent decisions and iterate safely over large collections. |
| Functions (definitions, parameters, return values, docstrings) | Encapsulate repeatable logic that can plug straight into pipeline steps. |

> Why these basics matter for MLOps
> • Clean variable scopes prevent side‑effects in multi‑step workflows.
> • Knowing the cost of each data type helps you optimize memory in containers.
> • Well‑designed functions turn notebook experiments into testable, deployable components.

#### Format & flow

1. Live code‑along — you’ll type every example with me.
2. Micro‑challenges — 5‑minute tasks after each section to cement concepts.
3. Recap & next steps — pointers to asynchronous practice and how these skills feed into later sessions (testing, packaging, CI/CD).

#### Access & duration

• First 2 hours (tonight’s entire fundamentals block) are free and public.
• Subsequent deep‑dives—type hints, virtual environments, packaging, and real pipeline wiring—continue exclusively for Bootcamp learners in later sessions.

#### Prerequisites

Access to Google Colab. No prior MLOps knowledge required.
Get ready to sharpen your Python toolkit—the foundation for everything we’ll build together!