Moussa Taifi: Clean Machine Learning Code: Practical Software Engineering... | PyData New York 2019 |
|
Full title: Moussa Taifi Ph.D: Clean Machine Learning Code: Practical Software Engineering Principles for ML Craftsmanship | PyData New York 2019
Machine learning pipelines are software pipelines after all. Their complexity and design viscosity lead to spectacular, costly and even deadly ML failures. This talk describes the most important Clean Code and Clean Architecture design principles, applied to machine learning applications. It aims to help the audience reduce machine learning technical debt, and to design robust ML architectures. www.pydata.org 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/YouTubeVideoTimestamps |