This talk covers the importance of building end-to-end machine learning pipelines from day one. What you will learn: - why we need a machine learning pipeline and when to use it; - ML pipeline building blocks covering training and inference; - engineering around failures and engineering for performance; - ML pipelines debugging and monitoring; - open-source Python libraries to save your time. For whom: - data scientists, data analysts, data engineers, machine learning engineers, data product owners, Python developers, working or willing to work with machine learning. Prerequisites: to get the most out of this talk, Data Science, ML, and Python experience is recommended