Productionize Research Oriented Code by Python

Explore a 28-minute PyCon US talk by Tetsuya Jesse Hirata on transforming research-oriented Python code into production-ready applications. Learn a four-step process for productionizing code, including understanding through code reading and documentation, modularizing into preparation, pre/post-processing, and calculation components, refactoring with test code, and creating final products. Discover the key differences between research and production code, and gain insights on improving performance and monitoring behavior after deployment. Ideal for Python engineers involved in R&D, data science, AI/ML, or data-oriented projects seeking to bridge the gap between research and production environments.

Intro
Background
Example of pre processing code
Example of calculation code
3 differences between research oriented code and production code
Code Reading / Code Documentation
Modularization outcome
Mapping each module into directory
Before refactoring the code
preprocess.py: the three ways to pre-process data
Request routing: clarity input and output and define URI from data
The Flow Chart of Transformation from Research Oriented code into WEB API
Request parameter check: write decorators with JSON Schema
The Flow Chart Transformation from Research Oriented code into WEB API
Error check use error handler functions to detect error by using Flask
Summarize 4 Step Transformation from Research Oriented code into Products