Efficient Python Production Workflows

Course details

Writing code can be easy, but maintaining a product is always a challenge. In this course, learn what it takes to efficiently manage your Python projects. Instructor Miki Tebeka delves into the ancillary tasks around Python programming, such as dependency management, development methodologies, metrics, logging, testing, and deployment. While these topics aren't strictly related to coding, they're essential to making sure your code is production ready. Learn how to tackle challenges related to dependency management, effectively approach testing, configure a logging system, design metrics, leverage different deployment strategies, and more.

Learning objectives
Working effectively with a team
Effective dependency management
Production vs. development environments
Determining which kinds of tests to use
Sending feedback to developers
Why logging is a valuable asset
Configuring a logging system
Deployment strategies
Using Fabric to automate deployment

Concepts

0. Introduction

01 - Creating efficient Python projects
02 - What you should know
03 - Using the exercise files

1. The Production Process

04 - Working together as a team
05 - Avoid mistakes
06 - Feedback loop

2. Dependecy Management

07 - The problem
08 - Package managers
09 - Production vs. development
10 - Internal PyPI vendoring
11 - Docker
12 - Challenge - Gunicorn
13 - Solution - Gunicorn

3. Testing

14 - What to test
15 - CI CD
16 - Development vs. CI environment
17 - Feedback to developers

4. Logging

18 - Eyes to production
19 - Python loggers
20 - Log configuration
21 - Dynamic configuration
22 - Structured logging
23 - Log aggregators
24 - Challenge - Configure logging
25 - Solution - Configure logging

5. Metrics

26 - What are metrics
27 - Types of metrics
28 - Designing metrics
29 - Metrics decorators
30 - Metrics systems
31 - Altering
32 - Challenge - report errors metrics decorator
33 - Solution - report errors metris decorator

6. Deployment

34 - main .py
35 - Deployment problems
36 - Deployment strategies
37 - Reverting deployment
38 - Use Fabric to automate deployment
39 - Continuous delivery

Conclusion

40 - Next steps

Efficient Python Production Workflows

Authors

Miki Tebeka