Mahan Hosseinzadeh- Prophet at scale to tune & forecast time series at Spotify - PyData London 2019

Prophet at Scale: Using Prophet at scale to tune and forecast time series at Spotify Mahan Hosseinzadeh Description At Spotify we have many time series and the challenge is to forecast them all considering the model tuning. In this talk I'm going to present how to distribute Prophet or other Python models to decrease runtime from days to hours. 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: github.com/numfocus/YouTubeVideoTimestamps

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