Description
Code produced by data scientists is under attack! There are a growing series of conference talks, Medium blog posts and business stakeholders telling a story of how changing business objectives are driving interest in production-level code. Production-level code is considered time-consuming to produce and limiting for the experimentation process needed to create amazing models. You're going to follow a workflow that deconstructs your experimentation workflow in a Jupyter notebook and helps you create production-ready ML pipelines. The talk is focused on an open source Python framework, called kedro that emphasises creating reproducible, maintainable and modular data science code.
Documentation: https://kedro.readthedocs.io/en/stable/ GitHub Repository: https://github.com/quantumblacklabs/kedro Slides: https://speakerdeck.com/yetudada/reproducible-and-maintainable-data-science-code-with-kedro?slide=18