Basic setup to offer a datascience prototype (e.g. machine learning model) behind an API using python, flask, and docker.
- serialized file of trained ML-model
- predict method that transforms the required input into a prediction using the trained ML-model
- docker and python 3 installed
- miniconda docker container including
- running python environment according to
environment.yml
- running
main.py
app within newly created environment - flask API that serves the datascience prototype
- swagger spec of API based on docstring of a predict function within
main.py
- running python environment according to