I'm going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery. The course runs under the flag of Andrei Neagoie. Andrei is a popular instructor on Udemy, with almost 200.000 students, and top reviews. For this course, he has paired up with Daniel Bourke, a self-taught Machine Learning Engineer from Australia. In this blog post series, I will jot down my thoughts on the course, and what I've learned.
Here are 5 links for this Friday: How to create a simple REST API with Python and Flask in 5 minutes - a beginner-friendly Python & Flask tutorial What is Phoenix LiveView - a good overview about the new experimental feature in Elixir's web framework For a Good strftime - if you quickly want to look up how to format a string in Python Introduction to CSS - Youtube series about CSS A Recap of Frontend Development in 2019 - what happened in 2019?
I'm currently creating some micro-service applications with Docker and Flask. Python is quite a beginner-friendly language, and Flask is easy to get started with. At the same time, it isn't straightforward to add more than basic features. For example, the tutorials use Flask-RESTful. Adding Swagger-UI to my “standard” Docker image is quite a hassle. You can use flask-swagger-ui, but then you have to figure out how to serve the static json file.
A few days ago, I created a Docker build for Flask with PostgreSQL (both with Alpine Linux and with Debian Linux). Installing psypcopg-2 binary (required for Postgres) requires you to build the package from source. Now the Docker image grows in size, as it still contains the build artifacts. The solution? Multi-stage Docker builds. Let's say we have the following docker-compose.yml file. There are two services: a Flask API called users and a Postgres database called users-db.