You’re about to ship your Python application into production using Docker: your
images are going to be critical infrastructure.
And that means you need to follow best practices—if you don’t, you risk wasting
quite a lot of money:
* You don’t want to waste hours every week waiting for slow builds.
* Or even worse, you might have a production outage or security breach,
costing your company orders of magnitude more money.
So how can you ensure your Docker image packaging isn’t wasting your team’s
time? How can you ensure you’re following best practices, for security,
efficiency, and debuggability?
*Live, online training on Docker best practices specifically for Python running
in production*
Based on many years experience using Docker, and a over a year's worth of
research into best practices for Docker packaging of production Python
applications <https://pythonspeed.com/docker/>
(https://pythonspeed.com/docker/), I am teaching a live online class on Docker
best practices for Python.
You'll learn:
* Security best practices.
* Make your images identifiable and builds reproducible.
* Python-specific details on packaging and debugging.
* Creating smaller images while still having fast builds.
* And much more!
The class involves plenty of hands-on activities!* *You'll be typing along with
me as I demonstrate live how everything works (and occasionally make a mistake,
so you can see how I recover from it). You'll also get to do hands-on exercises
to help you solidify your understanding.
*The class will take place on two afternoons (UK time) on June 11th and 12th.
You can **learn more about the class on the event page*
<https://www.eventbrite.com/e/production-ready-docker-packaging-for-python-developers-tickets-106092599822>*
(*https://bit.ly/36kBhhN).
—Itamar
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