Play all

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

Description:

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.

Productionize Research Oriented Code by Python

PyCon US

Add to list

#Conference Talks #PyCon US #Programming #Programming Languages #Python #Flask #Data Science #Data Preprocessing