Getting APIs into production can be a bewildering prospect. This post gives you tips and a template for using Flask in production.
If you work in the world of (or a world adjacent to) cloud software and are familiar with Python, the chances are you’ll have come across Flask — the excellent, minimal ‘ micro’ web framework that’s been at the heart of the Python web community for around a decade. It has found its way into a huge number of applications, reportedly including aspects of the LinkedIn and Pinterest platforms, as well as innumerable other commercial, private and research projects too. This popularity ensures it has a vibrant ecosystem of extensions, documentation and tutorials.
So why _another _Flask blog post? Over the last eight or so years, I’ve used Flask for dozens of personal and professional projects, and I’ve learned a lot. However, it can be difficult to find simple, good-quality guides on how to get started with a ‘production-ready’ Flask app. I thought I’d put together some of my thoughts on what a simple, minimal Flask ‘production-ready’ application could look like — a (not comprehensive) list of a few things I wish I’d have known over the years. If you have suggestions for improvements to the advice in this post, do make sure to get in touch and let me know.
Additionally, for this post I’m particularly focussing on Flask apps developed for the purpose of providing APIs as web services. This is a fundamental use-case for many software professionals, and it’s also increasingly relevant to many Machine Learning practitioners too. Plus it is something that can be a bit bewildering to new-comers to the software world. This post is therefore aimed at giving a simple but solid ‘production-ready’ Flask service template for you to build upon, and to share some rationale for the structure I’ve provided. I’ve also given a few basic steps to get the template project deployed to Google Cloud Run too. Let’s dive in.
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