Best Practices for Productionalizing Data & ML Projects | Tecton


Best Practices for Productionalizing Data & ML Projects

apply(conf) - Apr '21 - 10 minutes

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.
Michelle Ufford

CEO and Co-Founder


Michelle is Founder & CEO of, an early-stage startup building next-gen analytics infrastructure. Before starting Noteable, she led the Big Data Tools engineering team at Netflix, where she was responsible for platform innovation and analytics tooling for Netflix’s industry-leading data platform. Prior to that, she led data engineering, data management, and platform architecture for GoDaddy, where she set a TPS record for SQL Server and helped pioneer Hadoop data warehousing techniques.
Matthew Seal

Co-Founder and CTO


Matthew Seal is a co-founder and CTO of Noteable, a startup building upon his prior industry-leading work at Netflix. He began his career at OpenGov and helped build their data platform before quickly rising to lead architect. He then went to Netflix, where he had an opportunity to work on a variety of cutting-edge technologies & architectures at massive scale. Matthew holds an MS from Stanford in ML/AI & Robotics and is a thought-leader in the Jupyter community. He’s a core maintainer of many Jupyter and nteract projects such as papermill, and most recently testbook, and frequently presents related talks at conferences including PyCon, JupyterCon, & Spark Summit.

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