cdCon2021

Machine learning is a key technology used in various fields like Education, Science. Jenkins and its plugin ecosystem can play a key role in supporting, facilitating and accelerating the integration of ML workflow components and orchestrating their reproducible execution. Currently, Python and interactive computational notebooks are the dominant software tools for teaching, composing and executing machine learning workflows. However, interactive notebooks present significant limitations to the adoption of good software engineering principles (test-driven development, code versioning, reproducibility, etc) as well as scaling-up to data sizes typically used in DS production environments Integrating Machine Learning workflows with Jenkins build tasks. It integrates machine learning tasks and algorithms to build ML data systems that are easier to develop, test, deploy and maintain.