The more complex your ML project becomes, the more challenging it is to manage and deploy it into production. Beyond reproducibility, factors such as flexibility, readability, and production readiness play crucial roles in enhancing project efficiency. This is where Kedro comes in, a framework specifically designed to take your ML projects to another level.

In this talk, I will introduce Kedro and explain the contexts in which it should be used. You'll gain hands-on experience with Python, learning how to smoothly integrate Kedro into your projects. This will enable you to spend less time on tedious 'plumbing' work and more time focusing on solving new problems.