Taking Python to Production: A Professional Onboarding Guide

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Eric Riddoch

1:45:50

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1. Environment Setup

1.1 pap-book-preview.pdf

1. Linux and terminal crash course.html

2. Install VS Code.mp4

02:02

3. Make sure you have the `code` command.html

4. Mac users only Install Homebrew, Xcode, and iTerm2.html

5. Windows users only Install the Windows Subsystem for Linux (WSL2).mp4

06:19

6. Windows users only Integrate VS Code with the WSL2.mp4

07:40

7. Resources Installing git.html

8. Installing the git CLI tool.mp4

02:50

2. Improving the terminal with ZSH

1. Introduction.mp4

02:56

2. Installing OhMyZSH.mp4

03:23

3. Navigating using ZSH and installing ZSH plugins.mp4

02:42

4. The ~.zshrc file and ZSH themes.mp4

03:55

5. Disablingenabling ZSH plugins; All-in-one Markdown extension; web-search plugin.mp4

10:20

6. Typeahead auto-completion with zsh-autosuggestions.mp4

04:09

7. Syntax highlighting with zsh-syntax-highlighting.mp4

02:13

8. Cheat sheet quick ZSH setup.html

3. Managing multiple Python versions

1. Introduction to Semantic Versioning (semver).mp4

14:19

2. Semantic Versioning (continued).mp4

12:35

3. Semantic Versioning.html

4. Why developers need to be able to switch between multiple Python versions.mp4

05:32

5. Resources for installing pyenv.html

6. Installation and overview of pyenv for managing multiple Python versions.mp4

14:30

7. History of Python changes; Overview of how Python evolves.mp4

10:25

Description

Data scientists, analysts, and beginner devs: transition from "coder" to "software engineer" and learn to ship code

What You'll Learn?

Set up a professional Python development environment - Visual Studio Code, pyenv, pipx, git, autocompletion
Learn the professional git workflow with GitHub and CI/CD with GitHub Actions
Make the terminal more intuitive with ZSH and plugins
Version and package Python software and publish it for the community
Setup automated code quality checks (testing, linting, documentation, type checking, etc.)

Who is this for?

Lower-intermediate to advanced Python developers who meet the requirements and are interested in the learning outcomes.

Data scientists, analysts, junior developers, and self-taught developers who want want to set up a development environment for writing "production-ready" software

More details

Description
This is a course about transitioning from a "coder"Â to a "software engineer". It specifically covers the tools needed to develop and "ship" production-ready software with Python.
------ Note ------
You should know that this course is still unfinished the final section lists the sections that still aren't published. IÂ have a soft commitment to get everything published by April 2nd, 2023.
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As an MLOps engineer, my role is to help enable data scientists, analysts, and junior engineers become more self-sufficient at bringing products to production.
This course covers a mix of foundational tools, engineering practices, and career advice that new engineers should be given during the onboarding process when they join a team (but they often don't get guidance!).
By the end of this course, you should feel confident contributing to complex software projects in a team setting, whether open-source or at a company (or please request a refund within 30 days!).
You will understand how closed- and open-source projects are run and how to run your own.
In the course, we write very little code and instead focus on the non-coding aspects of software engineering that make you an effective member of the software engineering community. But you should have a solid grasp of Python fundamentals (loops, functions, classes, etc.)
Expect to learn
how to set up a professional Python development environment
how to set up a professional workflow for Python development with Visual Studio Code; extra emphasis on autocompletion
how to use git, GitHub, "branching strategies", and their integrations with VSÂ Code and the terminal
how to write clean, maintainable code and ensure that all code contributed to your projects is good quality (testing, linting, formatting, type checking, documentation, etc.)
how to publish production-quality software for a wide audience with packaging, versioning, continuous integration, and continuous delivery (pre-commit, GitHub Actions, PyPI)
how to templatize all of the above points, so you can create new, high-quality projects in seconds
Before paying for this course, please sample the preview lectures so you can get a sense of whether it's right for you.
See you in the course!
- Eric

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Important requirement:

Please do not buy this course if you do not have a machine capable of running a Linux terminal.

If you are running MacOSÂ or Linux (e.g. Ubuntu), you should have no problems getting access to a Linux terminal for this course. For Windows users it's trickier.
There is a free preview video about the Windows Subsystem for Linux. Watch this video to see if your version of the Windows operating system is capable of running a true Linux terminal. If you are a Windows user, you will need Windows 10 or 11.
Without an OSÂ compatible with a Linux terminal, you will not be able to take this course unless you do one of 3 things:

1. Switch to a Mac or Linux machine for this course
2. Upgrade Windows OS to a version that supports it
3. Dual-boot a Linux (e.g. Ubuntu)Â operating system on the same machine that you have Windows on

You can easily find guides for (2)Â and (3)Â online.
Who this course is for:
Lower-intermediate to advanced Python developers who meet the requirements and are interested in the learning outcomes.
Data scientists, analysts, junior developers, and self-taught developers who want want to set up a development environment for writing "production-ready" software

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Hi, I'm Eric. I've been working in the industry for just over 4 years--the first two years as a Data Engineer (Python, Airflow, Kubernetes, AWS, Snowflake), and the most recent two years as an MLOps engineer at a product placement company called BENLabs.I co-organize the Utah, USA chapter of the MLOps-Community Meetup. Join us if you're around!I have extensive experience architecting systems on AWS, making sure they are secure from a network and application standpoint; also that they are scalable, monitored, and tested. I've done a fair bit of work on CI/CD, writing and testing infrastructure-as-code, and full-stack development.

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