This talk will briefly explore the development lifecycle for data engineering & ML projects before delving into some of the friction points most common when productionalizing those projects. We’ll provide an overview of how large companies like Netflix & Amazon have addressed those challenges using tools like Jupyter notebooks, and we’ll also share some hard-won lessons learned from the trenches. Attendees should come away with an understanding of common patterns, suggestions for useful tooling, and practical approaches for productionalizing your data & ML projects.