Basak Eskili & Maria Vechtomova: ML Model Traceability and Reproducibility by Design скачать в хорошем качестве

Basak Eskili & Maria Vechtomova: ML Model Traceability and Reproducibility by Design

www.pydata.org Model traceability and reproducibility are crucial steps when deploying machine learning models. Model traceability allows us to know which version of the model generated which prediction. Model reproducibility ensures that we can roll back to the previous versions of the model anytime we want. We, as ML engineers, designed reusable workflows which enable data scientists to follow these two principles by design. PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases. 00:00 Welcome! 00:10 Help us add time stamps or captions to this video! See the description for details. Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/numfocus/YouTubeVi...